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Article

An Analysis of the Potential Regulatory Mechanisms of Sophora Flower Development and Nutritional Component Formation Using RNA Sequencing

by
Xuhong Song
,
Jirui Wang
,
Fanghong Shang
,
Gang Ding
and
Longyun Li
*
Chongqing Academy of Chinese Materia Medica, Chongqing 400065, China
*
Author to whom correspondence should be addressed.
Horticulturae 2023, 9(7), 756; https://doi.org/10.3390/horticulturae9070756
Submission received: 20 May 2023 / Revised: 24 June 2023 / Accepted: 27 June 2023 / Published: 30 June 2023
(This article belongs to the Special Issue The Physiology, Molecular Biology, Biochemistry and Photoсhemistry in Horticultural Plant)
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Abstract

:
Sophora flower (Huaihua) is the flower of Sophora japonica L., which is used in ethnic food and traditional medicine in China. Unfortunately, the molecular mechanism related to the nutritional quality and regulation of floral organ development has yet to be elucidated in Huaihua. To understand the molecular mechanism of the different developmental stages of Huaihua, this study evaluated the transcriptome analyses of five different developmental periods from Huaihua. A total of 84,699 unigenes were reassembled from approximate 50 million high-quality clean reads. The results showed that the phenylpropanoid biosynthesis, plant hormone signal transduction, starch and sucrose metabolism, and fatty acid elongation process pathways were strongly induced at different developmental stage genes in Huaihua. During this study, 394 differentially expressed genes (DEGs) were identified for further studies, which included 13 phenylpropanoid biosynthesis-related genes, 186 plant hormone signal transduction-related genes, and 195 starch and sucrose metabolism response genes. Regarding the peroxidase in the lignin synthesis pathway, CCoAOMT was significantly upregulated with the development of Huaihua. The enzyme genes in flavonoid synthesis, such as PAL, 4CL, flavonol reductase, and 3 GT, were significantly downregulated during Huaihua development. In addition, the results also indicated that the enrichment key genes in these pathways of Huaihua can be divided into two distinct parts at developmental stages. In the floral bud stage, flavonoid, auxin, and cytokine synthesis-related genes were highly expressed. In the mature bud and full flowering stage, the expression level of genes related to lignin, gibberellin, abscisic acid, and salicylic acid synthesis was high, while, for other genes related to flavonoid synthesis, it was lower. Furthermore, the DEGs in the starch and sucrose metabolism pathway were also significantly upregulated in the later stage of flower development. This study provides a preliminary and comprehensive assessment of the quality formation and flowering regulation mechanism in Sophora japonica L. by investigating the expression profiles of the critical flowering-related genes at different developmental stages. The results indicate that the regulatory genes in these key biological pathways could be crucial factors involved in Huaihua development, which can provide a reference and new insights with which to further understand the molecular mechanisms of flower development in Sophora japonica L.

1. Introduction

The flower of Sophora japonica L., a Leguminosae plant with pinnately compound leaves, can be cooked and eaten, as well as used in traditional Chinese medicine or dyes. People can distinguish between Huaimi (flower bud) and Huairui (mature period) according to the flower’s different development periods. S. japonica contains important medicinal active ingredients, such as flavonoids. Previous studies have shown that Huaihua has great medicinal effects and potential value, such as the potential to reduce obesity [1,2], aid in medical development related to osteoporosis [3], and inhibit airway inflammation. Huaihua also has great application in the relief of allergy and asthma symptoms [4], as well as having anti-inflammatory, antitumor [5], and antioxidant [6] properties and the ability to regulate the intestinal flora [7], protect the skin from UV damage [8], and assist in hypolipidemic drugs and other aspects [9,10]. However, knowledge about the flowering and quality formation of S. japonica is scarce, especially because there are differences in nutrients and content between Huairui and Huaimi [11]. The nutritional quality of S. japonica varies significantly in different flowering stages, which determines consumers’ choice and its value as a traditional Chinese medicine. Therefore, the gene network and the unigenes expression pattern involved in the different flowering stages of S. japonica are very important elements to consider when studying the mechanism responsible for quality formation and floral development.
The flowering process is the most important event in the plant lifecycle. Molecular and genetic studies of plant flowering have shown that the flowering process of plants is controlled by a variety of exogenous and endogenous factors with a complex genetic network. At present, transcriptome expression profiling is widely used in various high-throughput comparative studies [12]. Research on flowers has mainly considered the regulation of the flowering time, the regulation of different colors of flowers, and information on floral sex. However, less studies have explored the complex endogenous pathways and regulatory network during different periods according to the transcriptome profile. A previous study using a comparison of expression profiles identified 72 candidate genes related to flowering time in Capsella bursa-pastoris at different latitudes [13]. Other researchers have used RNA-Seq analyses of the flower of cultured cultivars in Narcissus pseudo narcissus [14], and have explored the effect of floral sex. Male flowering is related to the flavonoid biosynthesis pathway, while female flowering is related to the phytohormone signal transduction pathway [15]. Cytokinin triggers the initiation of female flower primordia, and other plant hormones jointly promote the development of female flora [15]. The current research on S. japonica mainly focuses on chemical composition, pharmacological effects, processing, and clinical research. Few researchers have studied the gene expression profile of an S. japonica flower at different developmental stages at the transcriptome level.
In order to study the mechanism responsible for the nutritional quality and flower development of S. japonica, transcriptome sequencing (RNA-Seq) was performed at five stages of flower development (Huaihua). In this study, gene functional annotation and differential expression gene analysis were conducted using transcriptome data. WGCNA analysis was performed to further screen for key genes and metabolic pathways associated with nutrient quality and the accumulation of floral development-related physicochemical indicators (metabolites). It is particularly important to study the gene expression profile during the key development of S. japonica flowering and quality formation.

2. Materials and Methods

2.1. Plant Materials and Samples Collection

Sophora japonica L. was planted in Da Zu District, Chongqing, China (E105.68, N29.56; altitude: 379 m). Professor Long-Yun Li from the Chongqing Engineering Research Center authenticated the plant. The S. japonica trees were cultivated under normal irrigation and fertilization field conditions using common disease and pest control at the field. The blooming period of S. japonica is about 10–15 days. According to appearance, size, and color, the flowering time of S. japonica can be divided into five stages: the original bud stage (S1), plump bud stage (S2), bud cracking stage (S3), late bud stage (S4), and blooming stage (S5) (Figure 1). The detailed classification criteria and morphological description were mentioned in a previous article [16]. Each stage sample comprised at least 100 flowers collected from one tree and three biological replicates were taken from the samples in each period. Sampling time was around 10:00 am in July. After picking the flower, it was quickly placed into a storage tube, frozen in liquid nitrogen, and preserved at −80 °C before being used for subsequent RNA extraction and sequencing.

2.2. RNA Extraction

Total RNA was extracted using the TRIzol method (Invitrogen, Carlsbad, California). Take the centrifuge tube and label it; then, grind the sample into powder in a liquid nitrogen environment, add 1 mL Trizol immediately, mix well, let stand at room temperature for 5 min, add 200 µL chloroform, turn upside down for 15 s, keep samples at room temperature for 3 min. Then, centrifuge at 12,000 r/min for 15 min, carefully draw the colorless supernatant to a new centrifuge tube, add an equal volume of isopropanol, mix gently, ice bath for 10–20 min, discard the supernatant, and suspend the precipitate with 1 mL of 75% ethanol. After that, centrifuge at 12,000 r/min for 10 min, and finally discard the supernatant. Then, centrifuge briefly, and add 30–50 µL of water to dissolve RNA. Verify the RNA’s quality using an RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, Palo Alto, CA, USA). Obtain high-quality RNA for each sample.

2.3. Sequencing and Transcriptome Assembly

cDNA libraries and transcriptome sequencing of different Huaihua samples were constructed using the Illumina Hiseq 4000 platform (Illumina, San Diego, CA, USA) developed by the Novogene Bioinformatics Institute (Beijing, China). N s more than 10% and low-quality reads were removed (Qphred 20 for >50% read). Regarding Sophora japonica L., without a reference genome, 781291280 clean reads were assembled into contigs; then, the Trinity program [17] was used to assemble high-quality reads into candidate unigenes. Read counts per gene were expressed as the expected number of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM), and box plots were used to depict the unigene expression [18].

2.4. DEGs’ Annotation and Bioinformatic Analysis

Various public databases were used to annotate the assembled unigenes, including NR (http://www.ncbi.nlm.nih.gov, accessed on 18 April 2023), Pfam (http://pfam.sanger.ac.uk/, accessed on 18 April 2023), and the KEGG (http://www.genome.jp/kegg, accessed on 18 April 2023), Swiss-Prot (http://www.expasy.ch/sprot, accessed on 18 April 2023), and String (http://string-db.org/, accessed on 18 April 2023). These databases had an E value above 10−5. Gene ontology (GO) annotated (http://www.geneontology.org, accessed on 18 April 2023) by Blast2go (http://www.blast2go.com/b2ghome, accessed on 18 April 2023) and COG (http://www.ncbi.nlm.nih.gov/COG, accessed on 18 April 2023) were used for classifications’ annotation. Each unigene expression (FPKM) was calculated, and a standard false discovery rate (FDR) <0.05 was used to screen the differentially expressed genes (DEGs).
We used the WGCNA package for standardized gene co-expression network analysis in R (v3.3.0). After calculating, genes in this experiment with FPKM values ≥1 in more than two sampling points were used. Regarding automatic network construction and module detection, we constructed a gene expression adjacency matrix to analyze the network topology of each gene and determined the appropriate threshold. In this experiment, the soft thresholding power was set to 7. By default, the blockwiseModule was used to obtain modules, interfacing network analysis with other data such as functional annotation and gene ontology, calculating topological overlap matrix. A characteristic gene network was constructed to express the relationship between each module, exporting a gene network to the external visualization software. Finally, Cytoscape v.3.0.0. was used to visualize the networks and screen out genes that were highly correlated with traits and modules.

2.5. Quantitative

To validate the transcriptomic results, Primer Premier 5.0 (www.premierbiosoft.com, accessed on 18 April 2023) was used to design the specific primers (Table S3). A total of 84,699 genes were screened. The SYBR Green PCR kit (Qiagen, Dusseldorf, Germany) was used as the test system to test the genes’ expression level. The cycling reaction was 94 °C for 2 min, followed by 35 cycles of 94 °C for 10 s, 55 °C for 10 s, and 72 °C for 30 s. Each gene was replicated three times, and actin was used as an internal control. This experiment used 2−ΔΔCt to test the differential expression of unigenes [19]. R software 3.1.3 (http://cran.r-project.org/, accessed on 18 April 2023) was used to analyze the qRT-PCR data, and the fragments per kilobase of transcript per million (FPKM) value using log2 (fold change) measurements were employed for normalization.

3. Results

3.1. Appearance Characteristic of S. japonica Flowers

S. japonica is widely cultivated throughout China and is often planted on the sides of houses and roadsides as a landscape tree. The bud and booming flower of S. japonica are generally called Huaimi and Huairui in Chinese, respectively. It generally blossoms in July or August, and the flowering period is generally 10–15 days. S. japonica has a raceme with calyx campanulate and five 15–35 cm-length denticles; moreover, the corolla is creamy-white and with 3–4 mm-long calyx. In order to understand the mechanism responsible for the flowering process and quality formation of S. japonica in more detail, the morphological changes of Huaihua floral buds (five flowering stages) were identified in our previous study [16], which correspond to the longitudinal bud (S1–S4) and one full flowering stage (S5) (Figure 1). Similar to the other flowering plants, the development of Huaihua first produces sepals; then, it inwardly produces petals, stamens, and pistils. Then, Huaihua continues to develop into inflorescence axis, and then into the bud and flowering stage (Figure 1), including the initial budding stage (S1), plump bud stage (S2), bud-cracking stage (S3), initial flowering stage (S4), and full flowering (blooming) stage (S5). As we found previously, the nutritional quality of flower buds in these developmental stages was significantly different. Therefore, transcriptome sequencing was carried out in these five stages to explore the quality formation mechanism of S. japonica.

3.2. Quality Assessment of Transcriptome Sequencing Data

To elucidate the mechanism of Huaihua development, transcriptome sequencing (RNA-seq) was performed for the five different flowering stages (S1–S5). A total of 794,746,528 raw reads were obtained from the 15 samples of Huaihua using RNA-Seq. Moreover, a total of 781,291,280 clean reads were obtained after the raw data were filtered, demonstrating that the Q30 value is more than 92%. The specific detection quality and GC content are shown in Table S1. According to the data, 84,699 unigenes were spliced and assembled, and the N50 value reached 2764 bp. The obtained unigene databases were annotated, and the results of the annotations are shown in Table S2 and Figure 2. The results indicate that the RNA-Seq data are of high quality and are reliable, making them eligible for further analysis.

3.3. Differential Expression Gene Analysis of Assembled Unigenes

The genes’ expression profile of the five different developmental stages of Huaihua were compared using the standard FDR < 0.05 and|log2Fold Change| ≥ 1 to screen for differentially expressed genes (DEGs). The results showed that there is a relatively small difference between the S1, S2, and S3 stages. However, with progressive growth, the difference between S4 and S5 is more pronounced, especially in the S5 stage (Figure 3A). In addition, there is almost no regulatory level difference between S2 and S3. The number of DEGs between S1 and S2, S3, S4, and S5 are 1444, 2860, 8865, and 22,851, respectively.
To understand the expression levels of the DEGs of each developmental stage in more detail, K-means analysis of the DEGs at each flowering stage was performed in the present study. The results of selected DEGs that met the criteria are shown in Figure 3B. All abovementioned DEGs are divided into four different clusters, among which cluster 1 contains 11,950 unigenes, cluster 2 contains 26,488 unigenes, cluster 3 contains 30,567 unigenes, and cluster 4 contains 15,557 unigenes. Among them, DEGs in cluster 1 were highly expressed in S4 stage. In cluster 2, DEGs were upregulated in the early stage of Huaihua development and downregulated in the mature stage. Cluster 3 was gradually upregulated with the development of Huaihua, and reached the maximum in S5 stage. The result also found that the expression level of DEGs in cluster 4 was opposite that to cluster 3, and gradually decreased with the development of Huaihua. The DEGs’ expression level of Huaihua appears to exhibit specific expressions at specific stages during development. Therefore, further analysis of these differentially expressed genes should be conducted.

3.4. KEGG Enrichment Analysis of Different Flowering Stages

KEGG enrichment analysis of Huaihua’s DEGs at different developmental stages was carried out to understand the related metabolite pathways during the flowering process. DEGs were enriched into four main pathways, which are shown in Figure 4. Regarding the S1, S2, S3, S4, and S5 comparison groups, the KEGG enrichment bubble chart indicated that DEGs significantly enriched phenylpropanoid biosynthesis, plant hormone signal transduction, starch and sucrose metabolism, and the fatty acid elongation process at various development stages of Huaihua. The results preliminarily suggest that these pathways play an important role during Huaihua’s development. Therefore, an in-detail analysis of these pathways is mentioned above.

3.5. Comparative Analysis of Phenylpropanoid Biosynthesis in Huaihua

Detailed analyses of the phenylpropanoid biosynthesis pathway were conducted to find enriched DEGs that were significantly expressed between each stage of Huaihua’s development. A total of 106 DEGs were screened in the developmental stages (S1–S5) of Huaihua (Figure 5). The result of the heat map shows that the DEGs in the phenylpropanoid biosynthesis pathway can be divided into two expression patterns. In the early development stage of Huaihua (S1, S2, S3), the DEGs show a high expression pattern, while, with the development of S. japonica, the expression level is downregulated. The other DEGs show a high expression pattern in the late stage of Huaihua’s development (S4, S5); however, with the flowering development, the gene expression is increased from a low expression level. The detailed analysis found that most of the key enzyme genes, such as POX and CCoAOMT in the lignin synthesis pathway, were significantly upregulated with the development of Huaihua. Some enzyme genes related to flavonol synthesis, such as 4CL, PAL, flavonol reductase, and 3 GT, were downregulated during flower development. These results suggested that, with the development of the flower, the substances begin to synthesize towards the direction of the lignin accumulation.
Flavonoids are an important and active medicinal ingredient in Huaihua, while, with the development of Huaihua, the accumulation of flavonoids is significantly decreased. Therefore, to explore the correlation between flavonoids and related DEGs’ expression, the content of flavonoids was detected, and weighted correlation network analysis (WGCNA) was used in this experiment.

3.6. Weighted Correlation Network Analysis (WGCNA) of Flavonoids in Huaihua

The content of flavonoids detected by our previous research and the transcriptome profile was used to perform WGCNA analysis (Figure 6). The WGCNA analysis results divided the entire genes set into 13 modules (Figure 6A). The accumulation patterns of total flavonoids (TFC), rutin, and narcissin are consistent, and they all gradually decrease as the flower grows and matures. In addition, the accumulation mode of quercetin and isorhamnetin is opposite to TFC, rutin, and narcissin. The green gene module and salmon gene module have a strong correlation with TFC, rutin, and narcissin, especially TFC (the correlation is more than 0.8). Thus, the results indicated that DEGs in these two gene modules are significantly related to the synthesis of flavonoids in Huaihua (Figure 6B). Further analysis of these two modules revealed that the green module contains 4113 unigenes and the salmon module contains 1359 unigenes.
Based on the gene annotations, and after combining the gene expression levels (FPKM > 10) of these two modules, a total of 13 DEGs that related to flavonoid biosynthesis were screened (Figure 6C, Supplementary File S1). These included one 4CL (4-coumarate-CoA ligase), one ANR (anthocyanidin reductase), one CCR (cinnamoyl-CoA reductase), one CHS (chalcone synthase), three FLS (flavonol synthase), one LAR (leucoanthocyanidin reductase), one F3′H (flavonoid 3′-monooxygenase), one PAL (phenylalanine ammonia-lyase), two E1.14.11.9 (naringenin 3-dioxygenase), and one LDOX (leucoanthocyanidin dioxygenase). After an analysis of their gene expression, these DEGs have been found to be consistent with the accumulation of flavonoids. These results indicated that the accumulation of flavonoids in S. japonica was regulated by these DEGs, which affected the nutritional quality of Huaihua at different developmental stages.

3.7. Comparative Analysis of DEGs Related to Plant Hormones in Huaihua

The result of our KEGG enrichment analysis found that most related DEGs were also significantly enriched in the plant hormone signal transduction pathway during Huaihua’s development. Therefore, the DEGs in the plant hormone signal transduction pathway were analyzed in this study. A total of 186 DEGs were screened for their involvement in plant hormone synthesis (Supplementary File S2). The expression level of DEGs was shown in Figure 7. It is obvious from the sample cluster analysis that, in the first three stages of Huaihua development (S1, S2, S3), hormone regulation-related gene expression trends are consistent, and significant differences were noted between the late development of Huaihua (S4, S5) and the early stage. The detailed analysis indicated that, in the early development of Huaihua, auxin synthesis and regulation genes are mainly involved, such as AUX and SAUR. Moreover, cytokinin regulatory genes are also involved, such as ARR-A, etc. However, in the later stage, the regulation genes of gibberellin, abscisic acid, and salicylic acid are significantly enriched. The regulation of plant flowering is a complex process, and this is confirmed by our transcriptome profile. In this study, the whole process of phytohormones regulating Huaihua’s flowering can be divided into two different response stages, one in the early stage (S1, S2, S3) and the other in the late stage (S4, S5). The present results can provide a theoretical reference for Huaihua’s flowering regulation.

3.8. The DEGs Related to Starch and Sucrose Metabolism in Huaihua

The expression level of DEGs in the starch and sucrose metabolism pathway was significantly changed during the flowering process of Huaihua. A total of 195 DEGs were identifiably related to starch and sucrose metabolism (Supplementary File S3). From the heat map and expression pattern analysis of these DEGs (Figure 8), it was apparent that most of the genes were significantly upregulated in the later stages of Huaihua’s development (S4, S5). There are also a small number of genes that are highly expressed in the early stage of Huaihua’s flowering. These included key genes such as E3.2.1.21 (beta-glucosidase), sacA (beta-fructofuranosidase), E2.4.1.13 (sucrose synthase), and UGDH (UD-Pglucose 6-dehydrogenase), which were developed in Huaihua and were highly expressed in the later stage. The results showed that the changes in starch and sucrose metabolism are mainly concentrated in the later development of Huaihua during the flowering process.

3.9. Quantitative Reverse Transcription PCR (qRT-PCR) Verification

Finally, quantitative reverse transcription PCR analysis was conducted on the expression of 18 important DEGs that are involved in the process under investigation (Figure 9). These DEGs are related to phenylpropanoid biosynthesis, plant hormone signal transduction, and starch and sucrose metabolism, and included other randomly selected genes. Others included CHS (chalcone synthase), 4CL (4-coumarate-CoA ligase), E1.14.11.9 (naringenin 3-dioxygenase), ANR (anthocyanidin reductase), LAR (leucoanthocyanidin reductase), FLS (flavonol synthase), IAA (Auxin-responsive protein IAA), E3.2.1.21 (be-ta-glucosidase), E2.4.1.13 (sucrose synthase), UGDH (UDPglucose 6-dehydrogenase), CCR (Caragana korshinskii cinnamoyl-CoA reductase), MYC2 (Senna alexandrina cultivar Sona MYC2-like transcription factor), E4.3.1.24 (phenylalanine ammonia-lyase), and E1.14.13.2 (flavonoid 3′-monooxygenase). The results of qRT-PCR are consistent with the transcriptome results.

4. Discussion

The flower of Sophora japonica L. is edible and can be used in traditional Chinese medicine. According to different flowering stages, it can be divided into Huairui (mature flower) and Huaimi (flower bud). Huaimi is a rich and important source of rutin and is easy to extract [14,15,20]. Modern pharmacological studies have shown that the active ingredients of Huaimi extract have a wide range of pharmacological effects, such as antibacterial, anti-inflammatory, antioxidant, antiviral, and antitumor [4,5,6,7]. In recent years, the research direction of S. japonica by domestic and foreign scholars has mainly focused on pharmacological chemical components and bio-medicinal active ingredients or pharmacological effects. There have been no reports on the transcriptome exploration of Huaihua’s developmental molecular mechanism. Therefore, this study aims to detect the differentially expressed genes of Huaihua in different stages of flower development using RNA sequencing. The result showed that the gene expression is specific during development.
Interestingly, we found that the five flowering stages can be easily divided into two groups to analyze the main differing pathways and the gene annotation results. Firstly, the DEGs related to flavonoid synthesis, the regulation of auxin synthesis, and cytokine were highly expressed in the floral bud stages (S1–S3). Then, with progressive growth, the late bud stage (S4) and full flowering stage (S5) could be defined as another group. In this group, the DEGs related to lignin, gibberellin, abscisic acid, and salicylic acid synthesis are upregulated, while some DEGs related to flavonoid synthesis are significantly downregulated (Figure 4, Figure 6 and Figure 7). Therefore, we further discussed the expression of these key genes and transcription factors separately. The relationship between key regulated genes (or TFs) and the development and medicinal quality of S. japonica was also discussed.

4.1. Phenylpropanoid Biosynthesis during Development of Huaihua

Phenylpropanoids play an important role in adaptation, including flavonoids and lignins, which are used in the development, growth, and reproduction of plants [20,21]. The content of flavonoids is the main active ingredient and quality index of Huaihua. In plants, both substances of flavonoids and lignin are synthesized from the phenylpropanoid pathway through sequential conversion and complex polymerization, respectively. Previous studies have shown that a total of 37 flavonoids or phenolic acids have the highest accumulation in S2 and S3 [22]. This is similar to the results of this experimental study. Most of these flavonoid components have a positive therapeutic effect on cardiovascular and liver diseases.
In this study, we also found that there were significant changes in the gene expression of flavonoids and lignin synthesis pathways in Sophorae japonica at different developmental stages. The expression of POX and CCoAOMT were upregulated in the early development stage of Huaihua development (S1, S2, S3), and were related to the lignin synthesis. Lignin is an important structural component of plant cell walls and vascular tissues and is commonly found in vascular plant cell walls [23]. However, in the present study, other DEGs (4CL, PAL, flavonol reductase, and 3 GT) were downregulated in the late stage (S4, S5); these enzyme genes were related to the synthesis of flavonoids. The function of flavonoids is related to biological development and stress resistance as signal molecules, which include flavones, flavonols, anthocyanins, and proanthocyanins [24]. These results suggested that, due to the upregulation of lignin synthesis DEGs and the downregulation of key genes of flavonoid synthesis during the development of S. japonica, the cell wall (cell strength) gradually increases during the development and maturation of S. japonica, while the medicinal quality gradually decreases.
In Huaihua, flavonoids are the main compound family that has been identified, with a total of 39 flavonoids and related glycosides. Among them, rutin (Querce-tin3-O-α-rhamnopyranosyl(1-6)-β-glucopyranoside) is the most important and abundant ingredient [25]. Due to its good medicinal effects, it has received attention from Chinese pharmacological researchers in recent decades [26,27,28]. The accumulation of flavonoids was significantly downregulated, which may explain why the S1–S4 stages of Huaihua are usually used in medicine. This result is consistent with the results of the key genes of flavonoid synthesis expression patterns. Therefore, these results suggested that the substance moves towards the direction of lignin synthesis, and that the synthesis of flavonoids shows a significant downward trend during Huaihua maturation. These may be the main factors affecting the quality formation of the S. japonica flower. This experiment is consistent with the results of previous studies that suggest that the content of total flavonoids and the expression level of related genes in S1 to S3 were significantly higher than those in S4 and S5 [22]. In the S2 and S3 stages, the buds of Huaihua are dilated and rich in flavonoids. Therefore, based on the analysis of this study, if one is only considering the accumulation of flavonoids when choosing Huaimi as a medicine, we highly recommend its use during the S1–S3 period (the entire length from the torus to the top, less than 6.0 mm).

4.2. Plant Hormone Signal Transduction Related to Huaihua Development

Flowering regulation is a very complex system which involves the regulation of many hormones [29,30]. Even though there are few transcriptome studies on continuous flowering, we can still find this regulatory law in the study of some flowering to fruiting or floral transitions [31,32]. In this experiment, the results also found that the expression of hormone-related DEGs in S. japonica varied greatly at different flower development stages. Huaihua’s developing expression trends of hormone-regulated genes are similar during the S1, S2, and S3 stages and were mainly related to auxin synthesis (AUX, SAUR) and the cytokinin regulatory gene (ARR-A). These results are consistent with a study which proved that the auxin response factor gene is involved in the regulation of the early flowering of plants [31].
In the later stage of Huaihua development, the two stages of S4 and S5 are significantly different from the previous stage, which mainly focused on the regulation genes of gibberellin, abscisic acid, and salicylic acid. Studies have found that the expression of gibberellic acid and jasmonic acid mainly acted in the early stage of fruit development, while the expression of auxin response factor genes increased almost during the whole process from bud to flower development [33,34]. This is slightly different from our study; however, previous transcriptomic studies on flowering and fruiting have mainly focused on the formation of floral primordia or changes from flower to fruit. The results of this study show that the regulation of gibberellin acid, abscisic acid, salicylic acid, and other related genes is more perturbed in the later stages of flower maturation (S4–S5), and that the regulation of auxin synthesis is upregulated mainly in the process of flower development relative to the early stage (S1–S3). This is in accordance with the relevant rules regarding the transition from flowering to fruiting [34]. Therefore, we speculate that the reason for the abovementioned experimental results is that few studies distinguish the flowering period as precisely. The plant hormone-related key genes, including AUX, SAUR, ARR-A, and the gibberellin, abscisic acid, and salicylic acid biosynthesis regulation genes play essential roles in Huaihua development and quality formation during different stages.

4.3. Starch and Sucrose Metabolism Related to Huaihua Development

Genes involved in starch and sucrose metabolism have also been identified as key regulators that affect Huaihua’s flowering process and quality formation. In the present study, the key genes UDP-glucose 6-dehydrogenase (UGDH), sucrose synthase (E2.4.1.13), and beta-fructofuranosidase (sacA) were highly expressed in the later stages of Huaihua’s development, which are involved in sucrose synthesis and control various developmental and metabolic processes in plants [35,36]. The present study was similar to previous research that suggested the sucrose plays an important role in the regulation of plant flowering [37]. In this study, five genes were annotated as trehalose-6-phosphate using KEGG enrichment analysis among the 195 DEGs that were involved in starch and sucrose metabolism. Trehalose-6-phosphate has been shown to regulate starch metabolism in plants and serves as a signal to induce flowering in Arabidopsis thaliana [38]. The gene annotated as trehalose-6-phosphate in this experiment is generally downregulated at the late flowering stage of Huaihua, which highlights the reliability of the results of this experiment and also illustrates that the connection between star and sucrose metabolism during Huaihua’s flowering period is a complex system. These results indicate that the quality formation of S. japonica in different flower development stages is regulated by these DEGs. The current research mainly compares the development of different plant tissues [39,40], and offers less research on expression at the different stages of the flowering process. The flowering of plants is a complex and huge system, and the specific pathways of Huaihua under the regulation of various hormones need to be explored in the future. Therefore, our results suggest that key genes such as UGDH, sucrose synthase, trehalose-6-phosphate, beta-glucosidase (E3.2.1.21), and sacA play a crucial role in regulating the development of medicinal (nutritional) qualities of S. japonica.

4.4. MADS-box, MYB, and bHLH Transcription Factors Related to Huaihua Development

The development of floral organs and flowering time are considerable agronomic characteristics in production and breeding when directly determining a plant’s commerciality, quality, and adaptability [41]. Previous studies have found that the proteins of MADS-box, bHLH, and MYB were related to plant and seed growth and development at various stages [42]. In this study, 10 MADS-box genes, 32 bHLH genes, and 29 MYB genes were identified to participate in Huaihua development. Among them, the gene family of the MADS-box transcription factor has been widely recognized as being able to control flower transition as well as to participate in floral organ development, reproduction, flowering time, flower formation, and fruit ripening in plants [43,44,45]. However, it remains unexplored in Huaihua flower development. Thus, it is important to study the molecular mechanism underlying the flower process for better application and a comprehensive understanding of the flowering time management to increase Huaihua productivity and quality. We found that these genes were expressed in specific floral tissues and flowering stages during the differentiation and growth of Huaihua. Three MADS-box genes (Clus-ter-4737.24442, Cluster-4737.25253, and Cluster-4737.24515) and five bHLH genes (Cluster-4737.25849, Cluster-4737.28106, Cluster-4737.35778, Cluster-4737.38149, and Cluster-4737.33317) had high expression levels, especially in the maturation process of Huaihua floral organs (Figure 10A,B). The gene expression of the transcription factor gene family bHLH, followed by MYB and WRKY, play crucial roles in plant flower development [46]. Therefore, these results indicated that the MADS-box, bHLH, and MYB genes might be main regulators in the later floral stages of Huaihua, participating in floral organ development and flowering time regulation [47].
In addition, studies have reported that these transcription factors were involved in the regulation of different branches of the phenylpropanoid pathway [48]. Moreover, MADS-box, bHLH, and MYB, are crucial to modulate plant anthocyanin biosynthesis [49]. In the phenylpropanoids’ regulatory pathway, the MYB transcription factor not only regulates the formation of plant secondary cell walls, but also regulates flavonoid metabolism (anthocyanin biosynthesis) [50]. The anthocyanins and volatile phenylpropenes (isoeugenol and eugenol) in flowers have a common precursor 4-coumaryl coenzyme A, indicating that the accumulation of phenolics at different stages of flower development is different [51]. The results of this experiment are similar to a previous study, in which the expression of five MYB genes (Cluster-4737.21173, Cluster-4737.22295, Clus-ter-4737.35253, Cluster-4737.39230, and Cluster-4737.39462) was high at the flowering stage (Figure 10C), which suggests that, as the flowering period gradually shifts to the mature stage, the synthesis of flavonoids may become downregulated [52].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae9070756/s1, Table S1: The specific detection quality and the GC content; Table S2: Annotations of the obtained unigene databases and the results of the annotations; Table S3: the specific primers used in Real-Time PCR; Supplementary File S1: Weighted correlation network analysis (WGCNA) analysis of the 13 unigenes related to flavonoid biosynthesis during the flowering process of Huaihua; Supplementary File S2: The 186 unigenes in the plant hormone signal transduction pathway during the flowering process of Huaihua; Supplementary File S3: The 195 unigenes in the starch and sucrose metabolism pathway during the flowering process of Huaihua.

Author Contributions

Methodology, software, validation, formal analysis, writing—original draft preparation, X.S.; data curation, investigation, writing—review and editing, J.W. and G.D.; resources, visualization, writing—review and editing, F.S., G.D. and J.W.; conceptualization, writing—review and editing, supervision, project administration, funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by China Agriculture Research System of MFO and MARA, grant number CARS-21; Chongqing Technical System of Chinese Medicinal Materials Industry, grant number 2021-[10]; Natural Science Foundation of Chongqing province, grant number cstc2020jcyj-msxmX0828; Chongqing Forestry key scientific and technological project, grant number 2016-14.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data and supporting sample-specific information discussed in this publication is available via the National Center for Biotechnology Information (NCBI) dataset accession number (PRJNA797104). We confirm that all materials and experiments in this publication were carried out in accordance with the relevant guidelines and regulations.

Acknowledgments

At the point of finishing this paper, the authors would like to express their sincere thanks to all those who contributed to the finished paper. The authors also would like to thank the Chongqing Academy of Chinese Materia Medica, which has allowed the researchers to perform the research activities in their laboratory, along with the support and funding from the Chongqing province’s other institutions.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Changes in phenotypes in the flower of Sophora japonica L. from the five different development periods. The longitudinal S1 = 2.8–3.8 mm, S2 = 4.0–5.1 mm, S3 = 4.5–6.0 mm, and S4 = 9.0–11.0 mm.
Figure 1. Changes in phenotypes in the flower of Sophora japonica L. from the five different development periods. The longitudinal S1 = 2.8–3.8 mm, S2 = 4.0–5.1 mm, S3 = 4.5–6.0 mm, and S4 = 9.0–11.0 mm.
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Figure 2. Venn diagram of differently expressed unigene annotations in each database. nr represents RefSeq non-redundant proteins, kog represents KOG database, go represents GO annotations, pfam represents Pfam database, and nt represents UniProt Knowledgebase.
Figure 2. Venn diagram of differently expressed unigene annotations in each database. nr represents RefSeq non-redundant proteins, kog represents KOG database, go represents GO annotations, pfam represents Pfam database, and nt represents UniProt Knowledgebase.
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Figure 3. (A) Comparative analysis of the differentially expressed genes in each period (S1–S5) of Huaihua, (B) K-means analysis of differentially expressed genes divided into 4 clusters. Cluster 1 includes 11,950 unigenes, cluster 2 includes 26,488 unigenes, and cluster 3 includes 30,567 unigenes, cluster 4 includes 15,557 unigenes.
Figure 3. (A) Comparative analysis of the differentially expressed genes in each period (S1–S5) of Huaihua, (B) K-means analysis of differentially expressed genes divided into 4 clusters. Cluster 1 includes 11,950 unigenes, cluster 2 includes 26,488 unigenes, and cluster 3 includes 30,567 unigenes, cluster 4 includes 15,557 unigenes.
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Figure 4. The KEGG enrichment analysis of differentially expressed genes (DEGs) in the S1, S2, S3, S4, and S5 stages of Huaihua: (A) KEGG enrichment pathways of the DEGs exclusively detected in S2 vs. S1. (B) KEGG enrichment pathways of the DEGs exclusively in S3 vs. S1. (C) KEGG enrichment pathways of the DEGs exclusively detected in S4 vs. S1. (D) KEGG enrichment pathways of the DEGs exclusively detected in S5 vs. S1. The red arrow represents different stages of Huaihua’s development process, mainly related to phenylpropanoid biosynthesis, plant hormone signal transduction, starch and sucrose metabolism, and fatty acid elongation process.
Figure 4. The KEGG enrichment analysis of differentially expressed genes (DEGs) in the S1, S2, S3, S4, and S5 stages of Huaihua: (A) KEGG enrichment pathways of the DEGs exclusively detected in S2 vs. S1. (B) KEGG enrichment pathways of the DEGs exclusively in S3 vs. S1. (C) KEGG enrichment pathways of the DEGs exclusively detected in S4 vs. S1. (D) KEGG enrichment pathways of the DEGs exclusively detected in S5 vs. S1. The red arrow represents different stages of Huaihua’s development process, mainly related to phenylpropanoid biosynthesis, plant hormone signal transduction, starch and sucrose metabolism, and fatty acid elongation process.
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Figure 5. The genes’ expression pattern in phenylpropanoid biosynthesis pathway at 5 different developmental stages of Huaihua (S1, S2, S3, S4, and S5). The deeper purple color represents a higher positive correlation; the darker the blue color is, the higher the negative correlation.
Figure 5. The genes’ expression pattern in phenylpropanoid biosynthesis pathway at 5 different developmental stages of Huaihua (S1, S2, S3, S4, and S5). The deeper purple color represents a higher positive correlation; the darker the blue color is, the higher the negative correlation.
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Figure 6. WGCNA analysis of DEGs at different flowering stages: (A) Hierarchical cluster tree shows co-expression modules. Each leaf in the tree represents one gene. Major tree branches constitute 13 modules represented by different colors. (B) Visualization of the eigengene network represents the relationships among the modules and the clinical trait weight. The hierarchical clustering dendrogram of the eigengenes shows the relationships among the modules. Numbers in each block diagram represent the correlation of different modules, and the darker the red color, the higher the correlation. (C) The gene expression level analysis in phenylpropanoid biosynthesis pathway at S1, S2, S3, S4, and S5 stages.
Figure 6. WGCNA analysis of DEGs at different flowering stages: (A) Hierarchical cluster tree shows co-expression modules. Each leaf in the tree represents one gene. Major tree branches constitute 13 modules represented by different colors. (B) Visualization of the eigengene network represents the relationships among the modules and the clinical trait weight. The hierarchical clustering dendrogram of the eigengenes shows the relationships among the modules. Numbers in each block diagram represent the correlation of different modules, and the darker the red color, the higher the correlation. (C) The gene expression level analysis in phenylpropanoid biosynthesis pathway at S1, S2, S3, S4, and S5 stages.
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Figure 7. The gene expression analysis of the plant hormone signal transduction pathway at 5 different developmental stages of Huaihua development: S1, S2, S3, S4, S5. The darker purple color represents a higher positive correlation; the darker the blue color, the higher the negative correlation.
Figure 7. The gene expression analysis of the plant hormone signal transduction pathway at 5 different developmental stages of Huaihua development: S1, S2, S3, S4, S5. The darker purple color represents a higher positive correlation; the darker the blue color, the higher the negative correlation.
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Figure 8. The gene expression analysis of the starch and sucrose metabolism pathway at 5 different developmental stages of Huaihua: S1, S2, S3, S4, S5. The darker purple color represents a higher positive correlation; the darker the blue color, the higher the negative correlation.
Figure 8. The gene expression analysis of the starch and sucrose metabolism pathway at 5 different developmental stages of Huaihua: S1, S2, S3, S4, S5. The darker purple color represents a higher positive correlation; the darker the blue color, the higher the negative correlation.
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Figure 9. The correlation analysis of qRT-PCR value and FPKM value used to verify RNA-Seq data. The abscissa represents the different sampling time; ordinate represents the expression.
Figure 9. The correlation analysis of qRT-PCR value and FPKM value used to verify RNA-Seq data. The abscissa represents the different sampling time; ordinate represents the expression.
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Figure 10. (A) The expression pattern of MADS-box transcription factors at 5 different stages of Huaihua development. (B) The expression pattern of bHLH transcription factors at 5 different stages of Huaihua development. (C) The expression pattern of MYB transcription factors at 5 different stages of Huaihua development.
Figure 10. (A) The expression pattern of MADS-box transcription factors at 5 different stages of Huaihua development. (B) The expression pattern of bHLH transcription factors at 5 different stages of Huaihua development. (C) The expression pattern of MYB transcription factors at 5 different stages of Huaihua development.
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Song, X.; Wang, J.; Shang, F.; Ding, G.; Li, L. An Analysis of the Potential Regulatory Mechanisms of Sophora Flower Development and Nutritional Component Formation Using RNA Sequencing. Horticulturae 2023, 9, 756. https://doi.org/10.3390/horticulturae9070756

AMA Style

Song X, Wang J, Shang F, Ding G, Li L. An Analysis of the Potential Regulatory Mechanisms of Sophora Flower Development and Nutritional Component Formation Using RNA Sequencing. Horticulturae. 2023; 9(7):756. https://doi.org/10.3390/horticulturae9070756

Chicago/Turabian Style

Song, Xuhong, Jirui Wang, Fanghong Shang, Gang Ding, and Longyun Li. 2023. "An Analysis of the Potential Regulatory Mechanisms of Sophora Flower Development and Nutritional Component Formation Using RNA Sequencing" Horticulturae 9, no. 7: 756. https://doi.org/10.3390/horticulturae9070756

APA Style

Song, X., Wang, J., Shang, F., Ding, G., & Li, L. (2023). An Analysis of the Potential Regulatory Mechanisms of Sophora Flower Development and Nutritional Component Formation Using RNA Sequencing. Horticulturae, 9(7), 756. https://doi.org/10.3390/horticulturae9070756

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