Introduction

Plants synthesize secondary metabolites to promote their own survival, and some of these compounds have antioxidant or antibiotic effects1,2. Several lines of evidence suggest that plant secondary metabolites, especially isoflavones, can improve the fitness of both humans and plants. Accordingly, a great deal of research has been conducted on isoflavones. Legume species, including the economically important crop plant soybean, are rich in isoflavones with estrogenic and antioxidant functions3,4. Within plants themselves, isoflavones play crucial roles in nodulation and nitrogen fixation5 and defense against environmental stresses6. In the context of human health, isoflavones, as phytoestrogens, can decrease the risk of menopausal symptoms, breast cancer, osteoporosis, dementia, and cardiovascular disease7,8,9,10,11,12,13.

Declining estrogen levels in postmenopausal women are associated with a variety of cutaneous changes, including dryness, wrinkling, poor healing, and hot flashes, many of which can be improved by estrogen supplementation14. However, the estrogens used in hormone replacement therapy can promote the initiation and progression of breast cancer15,16,17. The effects of estrogen are mediated by two estrogen receptors (ERs), ERα and ERβ, which are distributed differently in each tissue18,19. ERα mediates the breast cancer–promoting effects of estrogens, whereas ERβ inhibits breast cancer cell proliferation and tumor formation20. Therefore, it has been proposed that dietary or synthetic ERβ-selective estrogens would lack the breast cancer–promoting properties of the estrogens used in hormone replacement regimens20.

Coumestrol (CMS), a coumestan isoflavone, is the most abundant polyphenol in soybean leaves21 and functions as a phytoestrogen that is structurally and functionally similar to 17β-estradiol, an estrogen steroid hormone22,23,24,25. ER-binding assays revealed that CMS has a 15-fold higher binding affinity for ERβ than for ERα26. Intake of CMS is associated with reduced risk of breast cancer27; in addition, CMS prevents skin photoaging by suppressing FMS-like tyrosine kinase 3, which causes collagen degradation and skin wrinkling28. CMS can decrease melanin synthesis, which darkens the skin, as well as alleviate symptoms caused by excessive melanin synthesis, such as melisma, solar lentigo, dark spots, and freckles29. In light of these health benefits, CMS has been suggested as a promising dietary supplement that could prevent disease and improve the health of postmenopausal women.

CMS is a soybean phytoalexin that is present in soybean leaves and roots, rather than seeds, and CMS content varies depending on environmental conditions and growth stage21,30,31. CMS accumulates to high levels after drought stress in root, doubling the extent of mycorrhizal colonization32,33. Because other isoflavones, such as daidzein and formononetin, are involved in signaling in rhizosphere plant-microbe interactions, CMS has been implicated in drought tolerance in legumes, an effect mediated through communication with mycorrhiza33,34,35.

CMS is derived from the soybean isoflavone daidzein, via dihydrodaidzein and 2’-hydroxydaidzein, through two biosynthetic pathways that remain incompletely understood36,37. Because many environmental factors affect the biosynthesis and accumulation of isoflavones, and epistatic interactions among multiple QTLs with small individual effects are responsible for a large proportion of the variation, it has been very challenging to elucidate the genetics governing isoflavone biosynthesis38,39,40,41. Due to the important implications of soybean isoflavones and phytoalexins for plant defense and human health, it would be valuable to identify the enzymes responsible for CMS biosynthesis from its precursor, daidzein. Knowledge of these enzymes would facilitate successful manipulation of CMS levels in planta.

In this study, we measured the CMS contents of 31 soybean genotypes, and selected those with consistently high or low CMS contents for gene expression profiling. To shed light on the genetics of CMS biosynthesis, comprehensive RNA sequencing (RNA-seq) was conducted on the leaf tissues at growth stage R7, when CMS levels are highest42. Based on the differentially expressed genes (DEGs) between high-CMS and low-CMS genotypes, as well as their functional co-expression network, we identified candidate genes involved in biosynthesis of CMS from daidzein. Our results provide a set of target genes for manipulations aimed at increasing CMS levels in soybean cultivars, with the goal of improving the welfare of menopausal women from clinical and cosmetic perspectives.

Results

CMS contents in soybean genotypes

We measured CMS contents in leaf samples from 31 soybean genotypes collected at growth stage R7 in 2016; three replicates were performed for each genotype (Supplementary Fig. 1). CMS content varied from 0 to 1,650.55 μg/g. Among the 31 cultivars tested, four with high CMS content, Chamame (1,650.55 μg/g), Geomjeongsaeol (1,209.70 μg/g), SG-257 (1,173.11 μg/g), and Daewonkong (802.08 μg/g), and four with low CMS content, SS0903-2B-21-1-2 (30.32 μg/g), Haepum (39.79 μg/g), SS0905-2B-179-1-1 (48.98 μg/g), and Sinhwa (142.90 μg/g), were selected for repeated cultivation and testing. CMS contents were measured in 2017, again in three replicates, to verify the results from 2016 (Fig. 1A). Daewonkong and SS0903-2B-21-1-2 had the highest (1781.79 μg/g) and the lowest (3.81 μg/g) CMS contents, respectively, in 2017. These two genotypes with consistently high and low CMS contents for two years were further investigated by RNA-seq to reveal the genetics underlying CMS biosynthesis.

Figure 1
figure 1

Measurements of CMS content. (A) CMS contents in eight selected soybean genotypes with three replications. Nineteen (Daewonkong, red bar) and fourteen (SS0903-2B-21-1-2, blue bar) exhibited the highest and the lowest CMS contents in 2017, respectively. Error bars indicate standard deviation. (B) CMS contents in Daewonkong. The contents were measured at two different time points in each of the subsequent years (2016 and 2017), for all three replications. The CMS contents of Daewonkong leaf samples at growth stage R7 were measured on October 6 (R7-1, 2016) and 13 (R7-2, 2016) in 2016 and September 26 (R7-1, 2017) and October 10 (R7-2, 2017) in 2017. Error bars indicate standard deviation.

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RNA-seq and DEG profiling

To identify the differences in gene expression involved in phenylpropanoid biosynthesis between Daewonkong and SS0903-2B-21-1-2, we extracted total RNA from leaf tissues at developmental stage R7. A total of 237 and 245 million reads, encompassing 24 and 25 Gb, respectively, were generated per genotype and about 70% and 75% of total reads were properly mapped to the G. max reference genome sequence (www.phytozome.net/soybean)43 (Supplementary Table 1). Comparison of expression levels between Daewonkong and SS0903-2B-21-1-2 revealed 4,046 DEGs with FC value of at least 4 (1,629 DEGs with FC 8, 684 DEGs with FC 16 and 283 DEGs with FC 32) and, overall about 50% of DEGs were up-regulated in Daewonkong (45.7–52.6%) (Fig. 2A). The DEGs were annotated against the AgriGO genome locus background, and metabolic process was the most enriched GO term in all four DEG sets (Fig. 2C–F). KEGG pathway analysis revealed that ~40% and ~30% of DEGs were assigned to metabolic pathways and biosynthesis of secondary metabolites, respectively (Supplementary Fig. 2). These results indicate that the secondary metabolic pathways are differentially regulated between the two genotypes.

Figure 2
figure 2

Number of DEGs and GO enrichment analysis. (A) The numbers of DEGs detected between Daewonkong and SS903-B2-21-1-2. The DEGs up-regulated in Daewonkong and SS0903-B2-21-1-2 are indicated in blue and orange, respectively. Each column indicates the numbers of DEGs with 4-, 8-, 16-, and 32-fold changes, respectively, between Daewonkong and SS0903-B2-21-1-2. (B) The number of DEGs in the network of phenylpropanoid biosynthesis. GO enrichment of (C) 4-, (D) 8-, (E) 12-, and (F) 32-fold DEGs between Daewonkong and SS0903-B2-21-1-2. Green bar indicates background reference, and blue bar indicates the query of the DEGs.

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The expression of genes in the phenylpropanoid biosynthesis pathway

To characterize differences in the expressions of genes involved in phenylpropanoid biosynthesis, we searched KEGG (http://www.genome.jp/kegg/) for soybean homologs of key enzymes in this pathway. In the biosynthetic pathway of phenylpropanoid, starting from phenylalanine to daidzein and its byproducts (Supplementary Fig. 3), we identified 72 candidate soybean homologs. Among them, 41 were connected with each other in a functional co-expression network in the SoyNet database (http://www.inetbio.org/soynet/) (Fig. 3)44. Eight of the genes in this network were differentially expressed between Daewonkong and SS0903-2B-21-1-2, and thus represent the DEGs most likely to affect the content of CMS, an end product of isoflavone biosynthesis (Supplementary Table 3). Two of these genes, Glyma.16G149300 (LOC100811727) and Glyma.17G064400 (LOC100779668), were down-regulated in Daewonkong (log2FC −2.7 and −3.1), whereas the other six, Glyma.01G228700 (chalcone synthase, CHS7), Glyma.01G239600 (2-hydroxyisoflavanone dehydratase, HIDH), Glyma.11G010500 (4-coumarate:CoA ligase, 4CL13), Glyma.11G011500 (CHS8), Glyma.13G173500 (isoflavone synthase, IFS2), and Glyma.14G005700 (chalcone reductase, CHR14), were up-regulated (up to log2FC 6.5). Five of the up-regulated genes were tightly connected with each other in the co-expression network of the phenylpropanoid biosynthesis pathway (Fig. 3, Supplementary Fig. 4). Among the eight DEGs, seven in the upstream pathway of CMS biosynthesis (i.e., all except Glyma.16G149300) were used to predict candidate genes for the unknown pathway (Fig. 3).

Figure 3
figure 3

Soybean homologs in the network of the CMS biosynthesis pathway. EC numbers of key enzymes involved in biosynthetic pathways from phenylalanine to CMS are indicated in the boxes on the left. Dotted lines and rectangles indicate unknown pathways and enzymes. Homologous genes corresponding to each EC number are located on the right in the same row. DEGs up- and down-regulated in Daewonkong are indicated in red and blue, respectively. Connections in the co-expression network connecting the DEGs are highlighted by red lines. The color scale on the bottom indicates log2FC value.

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Identification of candidate genes for CMS biosynthesis

To identify candidate genes for biosynthesis of CMS from daidzein, we applied three prediction approaches (Supplementary Table 4). First, using the 41 genes in the network as guide genes (Fig. 3), we searched the whole soybean gene network of 40,812 genes for genes closely connected to the guide genes (“guide prediction”). Second, the genes closely connected to the seven DEGs involved in phenylpropanoid biosynthesis were identified using the same network. Third, candidate genes were predicted in the context of subnetworks consisting of central hubs and their neighbors, and connections between the hub genes and the seven DEGs were identified (“hub prediction”) (Supplementary Table 4)44. We then listed the top 20 genes predicted from each approach, along with their GO terms from three different databases. The three methods identified 3, 11, and 9 genes as DEGs, all of which were up-regulated in Daewonkong (Supplementary Fig. 5). Overall, 14 genes were predicted from the three prediction approaches, of which seven were identified by two or more approaches (Supplementary Table 5). These 14 candidate genes may play a key role in determining CMS contents in soybean.

Validation of gene expression level by qRT-PCR

We validated the expression levels of the 14 DEGs by quantitative reverse-transcription (qRT) PCR (Fig. 4). The qRT-PCR results were consistent with the RNA-seq data: all 14 DEGs up-regulated in Daewonkong in the RNA-seq data were also up-regulated in the qRT-PCR results (Supplementary Fig. 5).

Figure 4
figure 4

Validation of RNA-seq results by qRT-PCR. Left and right y-axes indicate FPKM values from RNA-seq (blue bar) and relative transcript abundance from qRT-PCR (orange bar). D and S on the x-axis represent Daewonkong and SS0903-2B-21-1-2, respectively. Bars indicate means and standard deviation of three biological replicates. Asterisk above each bar indicates statistical difference between genotypes, as determined by Student’s t-test (p < 0.05). (A) Glyma.01G135200, cytochrome P450, family 82, subfamily C, polypeptide 4. (B) Glyma.02G005600, myb domain protein 15. (C) Glyma.02G134000, carboxyesterase 13. (D) Glyma.02G307300, NAD(P)-linked oxidoreductase superfamily protein. (E) Glyma.03G147700, disease resistance–responsive (dirigent-like protein) family protein. (F) Glyma.09G269500, NAD(P)-binding Rossmann-fold superfamily protein. (G) Glyma.11G004200, alpha/beta-Hydrolases superfamily protein. (H) Glyma.11G070500, NmrA-like negative transcriptional regulator family protein. (I) Glyma.13G284900, organic cation/carnitine transporter4. (J) Glyma.13G285300, cytochrome P450, family 82, subfamily C, polypeptide 4. (K) Glyma.17G030400, MLP-like protein 423. (L) Glyma.18G220600, NAD(P)-binding Rossmann-fold superfamily protein. (M) Glyma.18G285800, NAD(P)-linked oxidoreductase superfamily protein. (N) Glyma.19G030500, HXXXD-type acyl-transferase family protein.

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Proposed model for the biosynthetic pathway from Daidzein to CMS

Based on guide prediction and hub prediction approaches, 14 DEGs were predicted to be involved in CMS biosynthesis (Supplementary Table 5). All 14 were highly up-regulated in Daewonkong, and qRT-PCR results were in close agreement with the RNA-seq data. These genes were mapped against three GO databases: agriGO, AtgO, and Uniprot-GO. Of the 14, four (Glyma.02G307300, Glyma.09G269500, Glyma.11G070500, and Glyma.18G220600) were mapped to steroid and flavonoid biosynthetic processes; two (Glyma.02G005600 and Glyma.17G030400) were related to stress responses; and eight genes were not previously mapped in any GO database. The biosynthetic reactions from daidzein to (3 R)-2′-hydroxydihydrodaidzein comprise a series of oxidations (NADPH → NADP+) and hydrolysis (Fig. 5)36,37. All four DEGs involved in steroid and flavonoid biosynthetic processes (Glyma.02G307300, Glyma.09G269500, Glyma.11G070500, and Glyma.18G220600), as well as Glyma.18G285800 (GO-unmapped), encode proteins that catalyze NAD(P) oxidation/reduction reactions. NAD(P)-linked oxidoreductases (Glyma.02G307300 and Glyma.18G285800) catalyze the transfer of electrons from one molecule to another using NADPH or NADP+ as a cofactor, and the NAD(P)-binding Rossmann fold (Glyma.09G269500, Glyma.11G070500, and Glyma.18G220600) is involved in catalysis of NAD(P)-dependent oxidation45,46. The NmrA-like protein encoded by Glyma.11G070500 contains two domains, including a Rossmann fold47. Therefore, we speculate that the proteins encoded by these seven genes catalyze the NADPH oxidation reactions starting from daidzein (Fig. 5).

Figure 5
figure 5

Candidate genes involved in the biosynthetic pathway from daidzein to CMS. The lists of genes in the blue boxes indicate the candidate genes involved in the pathways of CMS biosynthesis. The blue box at the top includes Glyma.01G135200, AT4G31940.1, cytochrome P450, family 82, subfamily C, polypeptide 4; Glyma.02G307300, AT1G59960.1, NAD(P)-linked oxidoreductase superfamily protein; Glyma.09G269500, AT2G45400.1, NAD(P)-binding Rossmann-fold superfamily protein; Glyma.11G070500, AT4G39230.1, NmrA-like negative transcriptional regulator family protein; Glyma.13G285300, AT4G31940.1, cytochrome P450, family 82, subfamily C, polypeptide 4; Glyma.18G220600, AT2G45400.1, NAD(P)-binding Rossmann-fold superfamily protein; and Glyma.18G285800, AT1G59960.1, NAD(P)-linked oxidoreductase superfamily protein. The blue box on the bottom left includes Glyma.02G005600, AT3G23250.1, myb domain protein 15; and Glyma.17G030400, AT1G24020.1, MLP-like protein 423. The blue box on the bottom right includes Glyma.02G134000, AT3G48700.1, carboxyesterase 13; Glyma.03G147700, AT5G42500.1, disease resistance–responsive (dirigent-like protein) family protein; Glyma.11G004200, AT1G47480.1, alpha/beta-Hydrolases superfamily protein; Glyma.13G284900, AT3G20660.1, organic cation/carnitine transporter4; and Glyma.19G030500, AT5G39090.1, HXXXD-type acyl-transferase family protein.

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Discussion

Soybean is one of the most important crops in the world due to its high production of protein and oil. In addition, it is a valuable nutraceutical ingredient because it contains several phytochemicals, including isoflavones, saponins, phenolic acids, and linoleic acids. In light of their contribution to human health and plant defense systems, these phytochemicals, especially isoflavones, are desirable target traits in soybean breeding programs48,49,50,51,52,53. Isoflavones, synthesized predominantly in legumes, attract rhizobial bacteria, initiate nitrogen-fixing root nodule formation, exert antifungal activity, and serve as metabolic precursors for major phytoalexins5,6,54,55. CMS, a coumestan isoflavone, decreases the risk of breast cancer by binding selectively to ERβ23,24,26. Therefore, CMS is a promising phytoestrogen for use as a selective estrogen receptor modulator (SERM)26.

The extreme variability of isoflavone contents among different environments has hindered elucidation of the genetic basis of isoflavone biosynthesis56,57,58. Indeed, in this study, even though plant samples were prepared at the same location over 2 years in three biological replicates in each year, variations in CMS content were observed between 2016 and 2017, indicating that this trait is environmentally sensitive (Supplementary Fig. 1, Fig. 1A). Therefore, we only used leaf samples from genotypes with relatively consistent levels of CMS over both years. Moreover, to ensure that leaf tissues containing the highest levels of CMS were used for RNA-seq, the CMS content in Daewonkong was measured at two different time points at developmental stage R7 in each year of cultivation (Fig. 1B); CMS content dramatically increases after the reproductive stage and peaks at R742. In 2016, CMS content increased about 3.5% as the leaves matured at R7, but in 2017 it increased about 41% only during R7. The Daewonkong leaf samples used for the second measurement in 2017, which had the highest CMS, were used for RNA-seq. The other genotype, SS0903-2B-21-1-2, exhibited consistently low CMS content in both cultivation years.

In total, we identified 4,046 DEGs between growth stage R7 leaf samples of Daewonkong and SS0903-2B-21-1-2. Out of eight DEGs in the functional co-expression network of the phenylpropanoid biosynthetic pathway (Fig. 3), six were up-regulated at least 8-fold in Daewonkong in comparison with SS0903-2B-21-1-2 (Fig. 2B), and five (CHS7, CHS8, CHR14, HIDH, and IFS2) in the downstream pathway are tightly connected with each other. Glyma.14G005700 (CHR14) is a candidate gene for an isoflavone QTL59, and Glyma.11G011500 (CHS8) and Glyma.13G173500 (IFS2) are differentially expressed under abiotic stress60. Moreover, the enzymes encoded by Glyma.01G228700 (CHS7), Glyma.11G011500 (CHS8), Glyma.13G173500 (IFS2), and Glyma14G005700 (CHR14) interact with each other to promote isoflavonoid synthesis61. According to our expression data and network analysis, along with the previously reported literature, seven of the eight DEGs (i.e., all but Glyma.16G149300) are likely to play key roles in the phenylpropanoid biosynthetic pathways with CMS as their final product (Supplementary Table 3).

The legume-specific isoflavonoid pathway includes several side pathways that overlap and compete with each other, and share daidzein as a central metabolite62. Among homologous genes involved in isoflavonoid biosynthesis with daidzein as the common precursor, four (Glyma.08G089500, Glyma.10G176700, Glyma.13G173600, and Glyma.16G149300) were connected in the co-expression network. Glyma.08G089500, Glyma.10G176700, and Glyma.13G173600 were minimally expressed in both genotypes, but Glyma.16G149300 was detected as a DEG. Glyma.16G149300 was down-regulated in Daewonkong, consistent with the higher level of CMS accumulation in this genotype (Fig. 3).

Other than the candidate genes that catalyze the NADPH oxidation reactions starting from daidzein, there are seven more candidate genes involved in the biosynthesis pathway of CMS. MYB domain protein 15 (Glyma.02G005600) and major latex protein (MLP)-like protein 423 (Glyma.17G030400) are annotated as involved in stress responses. Because the regulation of isoflavonoid metabolism is thought to occur primarily at the level of transcription, transcription factors (TFs) are promising candidates63. Multiple MYB TFs regulate the expression of structural genes involved in isoflavone biosynthesis under stressed conditions64,65,66. MLP-like protein modulates the production of metabolites under drought stress conditions, and overexpression of MLP leads to salt stress insensitivity67,68. This agrees well with reports that CMS increases drought stress through communication with mycorrhiza and CMS accumulates the most at R7 growth stage when soybean is dehydrated while maturation33,42,69. Thus, these two genes may induce accumulation of CMS under stress conditions.

For candidate DEGs unmapped to any GO term, Glyma.01G135200 and Glyma.13G285300 are annotated as cytochrome P450, an oxidoreductase, that is reported to be involved in isoflavonoid biosynthesis70,71. Glyma.11G004200, annotated as a member of the alpha/beta-hydrolase superfamily, may play a role in the hydrolysis reaction of CMS biosynthesis. 2-hydroxyisoflavanone dehydratase, which catalyzes a dehydration reaction yielding isoflavone from 2-hydroxyisoflavanone, is a member of the carboxyesterase family (Glyma.02G134000)72. To date, although no evidence for their direct roles in isoflavone biosynthesis has been reported, these genes could also affect the biosynthetic pathway of CMS in as-yet-undiscovered ways (Fig. 5).

The functions of the candidate genes remain to be experimentally verified. The biosynthetic pathways responsible for production of anthocyanins, another class of soybean flavonoid metabolites, have been well studied due to the importance of these compounds for human health and the cosmetic industry. Anthocyanin content in soybean seeds is determined by six loci, and all genes corresponding to these loci have been isolated73,74,75,76,77,78. In addition, the key enzymes could be characterized by forward genetic approaches because anthocyanin levels can be easily distinguished based on the color of the seed coat or flower. However, because variations in CMS content do not cause any visible phenotypic variation, identification of the candidate genes involved in CMS biosynthesis is essential for study of the biosynthetic pathway using reverse genetic approaches. Knockout or down-regulation of each candidate gene, or multiple genes at the same time, would enable characterization of their effects on CMS accumulation. In vitro conversion of intermediate products with candidate enzymes might also be a good approach to characterizing the role of each enzyme in CMS biosynthesis.

In summary, to determine the genetic basis of CMS accumulation, we sequenced RNA samples from two genotypes, Daewonkong and SS0903-2B-21-1-2, which had consistently high and low CMS content, respectively. Using a co-expression network database and key DEGs identified in the iso/flavonoid biosynthetic pathway, we identified genes that might play important roles in CMS accumulation. Our results provide a valuable resource to help elucidate CMS biosynthesis in soybean and develop soybean cultivars with desired CMS contents, with the aim of improving plant defense and human health. Future research will focus on functional validation of the identified genes and complete characterization of the CMS biosynthetic pathway.

Materials and Methods

Plant materials for HPLC analysis

For CMS measurements, 31 soybean genotypes were planted at the Seoul National University Experimental Farm in Suwon, South Korea (37.3 °N, 127.0 °E), with three replications per cultivar, in 2016 (Supplementary Fig. 1). Average temperature and duration of sunshine in Suwon from May to October of 2016 were 16.1–28.0 °C and 1,293.6 hr, respectively. In 2017, eight genotypes, including four with high CMS and four with low CMS in 2016, were planted again in three replicates (Fig. 1A). Average temperature and duration of sunshine in Suwon from May to October of 2017 were 16.4–26.9 °C and 1,334.4 hr, respectively.

Soybean leaves collected at growth stage R7 were dried and ground to a fine powder. Each sample (3 g) was stirred in 45 mL of 80% ethanol for 1 day at room temperature, and then the mixture was filtered through a 0.45 μm GHP membrane filter (Acrodisc 13 mm syringe filter; Pall Corporation, Port Washington, NY, USA). Quantitative analysis of CMS was performed on a Mightysil RP-18 GP reversed phase column (5 μm, 4.6 × 250 mm) (Kanto Chemical Co., Tokyo, Japan) at sub-ambient temperature, using a 40 min linear gradient of 0.1% glacial acetic acid in water (solvent A) and 0.1% glacial acetic acid in acetonitrile (solvent B). The linear gradient program was as follows: 0–5 min, 31% B; 5–25 min, 31–35% B; 25–30 min, 35–80% B; 30–35 min, 80% B; 35–40, min back to 31% B. The solvent flow rate was 1.0 mL min–1, and the injection volume was 10 μL. UV absorption was measured at 342 nm to detect CMS (the standard purchased from Sigma-Aldrich Co., St Louis, MO, USA).

RNA-seq

Total RNA was extracted from growth stage R7 leaf samples of Daewonkong and SS903-B2-21-1-2 in 2017 using Ribospin™ Plant (GeneAll, Seoul, Korea). Three cDNA libraries per genotype were constructed using the TruSeq® RNA Sample Prep Kit v2 (Illumina Inc., CA, USA). The quality and quantity of samples used for sequencing were checked using a 2100 Bioanalyzer (Agilent Technologies). RNA samples were sequenced using the TruSeq SBS kit v3 on the Illumina HiSeq. 4000 platform. The raw RNA reads of Daewonkong (SRR6756974, SRR6756973, and SRR6756972) and SS903-B2-21-1-2 (SRR6756971, SRR6756976, and SRR6756975) have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (Supplementary Table 1).

DEGs and enrichment analysis

Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values were calculated by mapping raw RNA reads for 56,044 genes to the G. max reference genome annotation data (Gmax_275_Wm82.a2.v1.gene.gff3) using the Tuxedo software suite79. DEGs were defined as genes with a log2 fold change (FC) ≥ 2 between two samples in pairwise comparisons for three replications with p-value < 0.05 (Fig. 2A). FPKM values < 1 were converted to 1 for the purpose of calculating FC. Sets of DEGs with log2FC ≥ 2, 3, 4, or 5 were used for Gene Ontology (GO) enrichment analysis using the Singular Enrichment Analysis (SEA) tool, available at agriGO (http://bioinfo.cau.edu.cn/agriGO/), and KEGG (Kyoto Encyclopedia of Genes and Genomes) ontology (http://www.genome.jp/kegg/tool/map_pathway1.html) (p < 0.05) (Supplementary Fig. 2). The G. max reference annotation (Wm82.a2.v1) was used as a background reference for enrichment analysis.

Candidate gene prediction

New candidate genes in the phenylpropanoid biosynthetic pathway were identified using the soybean co-expression network consisting of 1,940,284 co-functional links between 40,812 soybean genes constructed based on 734 microarrays and 290 RNA-seq data from soybean, available at http://www.inetbio.org/soynet/ 44. First, new candidate genes in the pathway were identified by searching genes closely connected to the 41 known genes in the pathway as guide genes in the co-expression network (“guide prediction”). Second, the seven DEGs among the 41 genes in the pathway were used as guide genes for the same function. The genes were prioritized according to the sum of their log-likelihood scores, and highly ranked genes were considered good candidates for new members of the pathway44. Third, DEGs was used to identify new candidate genes through subnetworks consisting of a central hub and their neighbors (“hub prediction”). If significant overlap was observed between DEGs and neighbor genes of a subnetwork, the central hub of the subnetwork was identified as a candidate gene. Candidate genes were prioritized according to their p-values.

qRT-PCR validation of DEGs

Gene-specific primers for qRT-PCR analysis were designed based on the nucleotide sequences of selected DEGs using Primer3 (http://primer3plus.com/) (Supplementary Table 2). cDNA was synthesized using an iScript™ cDNA Synthesis Kit (Cat. 170-8891; Bio-Rad, Hercules, CA, USA). qRT-PCR was conducted using an iQ™ SYBR Green Supermix kit (Cat. 170-8882; Bio-Rad) on a LightCycler® 480 (Roche Diagnostics, Laval, QC, Canada). Actin was used for normalization of target gene expression, and each sample was analyzed in triplicate. Relative gene expression was analyzed based on the reference gene as previously described80. Student’s t-test was performed to determine whether differences were statistically significant (p < 0.05).