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Article

Flavonoid Biosynthesis in Scutellaria baicalensis Georgi: Metabolomics and Transcriptomics Analysis

by
Dongzuo Yao
1,
Jiaqian Xing
1,
Qingye Tang
1,
Yue Hou
2,
Binbin Chen
3,
Wenmiao Yao
4,
Zhenfang Li
1,
Jiaxing Wang
1,
Yanbing Niu
1 and
Defu Wang
1,*
1
College of Life Sciences, Shanxi Agricultural University, Jinzhong 030600, China
2
Chinese Herbal Medicine Industry Development Center, Jincheng 048000, China
3
Rural Social Affairs Center, Jincheng 048000, China
4
Modern Agricultural Development Center, Jincheng 048000, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(7), 1494; https://doi.org/10.3390/agronomy14071494
Submission received: 3 June 2024 / Revised: 29 June 2024 / Accepted: 8 July 2024 / Published: 10 July 2024
(This article belongs to the Special Issue Secondary Metabolites in Medicinal Edible Plant Cultivation)
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Abstract

:
Scutellaria baicalensis Georgi (SB), a plant of the Lamiaceae family, contains flavonoids with potent human health benefits. The full mechanistic details and regulatory networks related to the biosynthesis of these compounds in SB have been the focus of recent research but are still fragmented. Similarly, a complete account of the metabolites produced, specifically flavonoids, and their distribution in different parts of the plant is incomplete. To provide a more complete picture, herein we have explored the SB metabolites and differentially expressed genes in underground and aerial tissues. Of the 947 metabolites identified, 373 were differentially accumulated flavonoids (DAFs), and 147 of these were differentially accumulated in roots relative to other tissues. Interestingly, roots accumulated more baicalin and baicalein than aboveground tissues, but they were low in scutellarein and wogonoside, in contrast to previous reports. These differences may be attributed to either plant variety, age of the plants, or the extraction protocol. Transcriptomics analysis identified 56 key genes from the flavonoid synthesis pathway in all six SB plant tissues. A weighted gene correlation network analysis conducted using four DAFs (baicalin, baicalein, scutellarein and wogonoside) produced 13 modules. Baicalin and baicalein were positively correlated with one of these modules, whereas wogonoside and scutellarein were correlated with three other modules. Gene expression in these modules was consistent with the observed accumulation of these compounds in plant tissues. Fourteen structural genes were highly correlated with baicalin, baicalein and scutellarein, and 241 transcription factors (TFs) associated to these four compounds. The 13 highly correlated structural genes and 21 highly correlated TFs were used to construct correlation networks, where genes were identified to be highly correlated with flavonoid biosynthesis genes. Overexpression of some of these genes, namely, SbMYB8 (Sb02g25620), SbMYB14 (Sb09g00160) and SbbHLH94 (Sb07g11990), in SB callus increased flavonoid content and regulated the expression of genes involved in the flavonoid biosynthesis pathway, confirming their association to flavonoid production. Overall, the present work contributes to delineating the differences in flavonoid biosynthesis among different SB tissues.

1. Introduction

The genus Scutellaria (Lamiaceae) includes about 350 species of herbaceous plants and has been used in traditional medicine for millennia. The species Scutellaria baicalensis Georgi, referred to hereafter as ‘SB’, mainly grows in East Asia [1] and its dry root has been used as a multi-purpose herb in traditional Chinese medicine, as mentioned for the first time in the records of ‘Classic of Herbal Medicine’ (ShenNong Ben Cao Jing) around 200 AD [2,3]. Recently, new biomedical applications for SB extracts have been reported [4,5,6]; therefore, demand for this plant is growing. The beneficial properties of SB are attributed to the presence of, among other compounds, flavonoids. For example, baicalein and wogonin are two of its most abundant free flavonoids, whereas baicalin (a glycoside of baicalein the first pure compound isolated from SB) and wogonoside (a glucuronide) are also abundant [7].
In plants, about 6000 types of flavonoids have been identified which are distributed in six subclasses: flavones, anthocyanins, isoflavones, flavonols, flavanols and flavanones [8,9,10,11,12]. Flavonoids are important secondary metabolites in plant tissues, where they are involved in growth, development and maturation [13,14], and also in the regulation of responses towards abiotic or biotic stress [15]. For example, anthocyanins are essential for the development of flower color that attracts pollinator insects, whereas flavonols improve plant tolerance to cold and drought, and accumulate in epidermal tissues to provide resistance to UV radiation [16,17]. Because of their anti-inflammatory and antioxidant activities, flavonoids are essential components in nutritional products, pharmaceuticals and cosmetics. When included in the human diet, they help prevent or alleviate cardiovascular disease and arteriosclerosis [18] and also cancer, Alzheimer’s Disease (AD) or coronary heart disease [19,20].
The Kyoto Encyclopedia of Genes and Genomes (KEGG) databases show that flavonoids may be synthesized via multiple metabolic pathways, e.g., those for the biosynthesis of flavone and flavonols (ko00944), anthocyanins (ko00942), isoflavonoids (ko00943), phenylpropanoids (ko00940) and flavonoids (ko00941). In SB aerial organs, scutellarein and scutellarin are the predominant flavones, whereas in roots, the most abundant are baicalein, baicalin, wogonin, wogonoside, norwogonin and norwogonoside [21]. The intensively studied baicalein, wogonin and their respective glycosides, baicalin and wogonoside, lack a 4′-OH group on their B-rings which is present in the 4′-hydroxyflavones like scutellarein [3].
The synthesis of flavones in SB follows two distinct pathways. In the aerial tissues, phenylalanine is converted to cinnamic acid by phenylalanine ammonialyase (PAL), followed by a ring hydroxylation by cinnamoyl 4 hydroxylase (C4H) to produce coumaric acid. The latter is activated by p-coumaroyl CoA ligase (4CL), and after the action of chalcone synthase (CHS) and chalcone isomerase (CHI), naringein is produced [22]. Flavone synthase II-1 (FNSII-1) oxidizes naringenin to apigenin, which is further hydroxylated (to form scutellarein) and glycosylated (to form scutellarin) [23,24]. In contrast, the root-specific flavone (RSF) biosynthesis is a newly evolved pathway that produces 4′-deoxyRSFs [25]. Here, cinnamic acid is first converted to cinnamoyl-CoA by cinnamate-CoA ligase-like 7 (CLL-7), which is condensed with three malonyl CoA molecules by chalcone synthase (CHS-2). This results in pinocembrin, a flavanone without a 4′-OH, after isomerization by chalcone isomerase (CHI). Pinocembrin is converted to the precursor of other 4′-deoxyRSFs, chrysin, by an isoform of flavone synthase II-2 (FNSII-2) [24]. Chrysin is hydroxylated by flavone 6-hydroxylase (F6H) or by flavone 8-hydroxylase (F8H) to produce baicalein or norwogonin, respectively. Norwogonin produces wogonin by the action of phenylpropanoid and flavonoid O-methyltransferases (PFOMT) [26,27]. The enzyme flavonoid 7-O-glucuronosyltransferase (UBGAT) then produces baicalin (from baicalein) or wogonoside (from wogonin) [23].
The biosynthesis of flavonoids in plants is regulated by transcription factors (TFs). The MBW complex consists of three kinds of TFs: myeloblastosis proteins (MYB), basic Helix–Loop–Helix (bHLH), and WDR (WD40 repeat proteins). MYBs regulate the expression of structural genes such as CHS2, CHI, 4CL and PAL2, and therefore, the accumulation of flavonoids in tissues [28,29]. In SB, MYB3 promoted root-specific flavone biosynthesis [30] and MYB12 enhanced baicalin production [31]. In response to light, SB TFs SbMYB45 and SbMYB86.1 bind to cis-acting element MBSII of the promoter of flavone biosynthesis gene CHI to increase its transcription and enhance flavone content [32]. In addition, in transgenic tobacco, overexpression of SbMYB2 and SbMYB7 induced the accumulation of phenylpropanoid and enhanced resistance to oxidative stress, drought and salt [33]. In Muscari armeniacum, expression patterns of MYBF and MYB1 were related to those of flavonoid structural genes FLS and DFR, affecting flavonoid biosynthesis and flower coloration [34]. In chrysanthemum (Chrysanthemum × morifolium), MYB11 activated CHS2, CHI, DFR, ANS and FNS to increase levels of anthocyanins and flavonols [35]. In Malus crabapple, MYB8 activated the FLS promoter to regulate flavonol biosynthesis [36]. In pear fruit, MYB17 promoted flavonoid accumulation [37].
Several transcriptome profiling studies have been reported on flavonoids in SB and other plants [28,31,38,39,40,41]. The latest [41] studied flavonoid biosynthesis regulation in both below- and above-ground tissues. In particular, focusing on the proteomics aspect and including the role of protein phosphorylation, but less on the type and differential abundance of the metabolites generated. Thus, our aim herein was to generate a complementary database including comprehensive metabolomics and transcriptomics for tissues, some of which were not included in that paper, such as mature and immature seeds during the same harvest season.
To this end, differentially accumulated metabolites (DAMs) and differentially expressed genes (DEGs) were identified in pairwise comparisons of tissues. Guided by the Weighted Correlation Network Analysis, genes related to baicalin, baicalein, wogonoside and scutellarein were screened to elucidate a regulatory network of flavonoid synthesis. Finally, the involvement of particular genes in flavonoid synthesis was verified using an Agrobacterium-based genetic transformation system.

2. Materials and Methods

2.1. Plant Materials

SB was planted in the traditional Chinese medicine incubation base, Shanxi Agricultural University, China. Six tissues (roots, stems, leaves, petals, immature and mature seeds) of three-year-old SB plants were harvested, and we used three replicates for each sample. These eighteen sets of plant tissue samples were frozen in liquid nitrogen and stored at −80 °C for subsequent analysis.

2.2. Sample Preparation and Extraction for Metabolomics Analysis

The six tissues of SB were freeze-dried and crushed in a pulverizer (30 Hz, 1.5 min, MM400, Retsch, Shanghai, China). The powder (50 ± 0.1mg) was dissolved in 1.2 mL of 70% methanol aqueous solution pre-cooled at −20 °C, and allowed to stand at 4 °C for 8 h. Subsequently, the mixtures were vortexed six times for 30 s every 30 min at room temperature. After centrifugation (Eppendorf AG 22331 Hamburg, Germany, 12,000 rpm, 3 min), the supernatant was filtered using a 0.22 μm microporous membrane for ultra-high performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) analysis.

2.3. UPLC Conditions and ESI-QTRAP-MS/MS

The supernatants were analyzed using the UHPLC ExionLCTM AD system (https://sciex.com.cn/, accessed on 11 October 2023) coupled to the QTRAP 4500 LC-MS/MS (SCIEX, Carlsbad, CA, USA). The following conditions were used: Agilent SB-C18 (2.1 mm × 100 mm 1.8 μm) column (Agilent, Santa Clara, CA, USA); mobile phase A: pure water containing 0.1% formic acid; mobile phase B: acetonitrile containing 0.1% formic acid; gradient: 0 min V(A)/V(B) (95:5), 9 min V(A)/V(B) (5:95), 10 min V(A)/V(B) (5:95), 11.1 min V(A)/V(B) (95:5) and 14 min V(A)/V(B) (95:5). The flow velocity was 0.35 mL/min, the column temperature was 40 °C and the injection volume was 4 μL. The effluent was alternatively connected to ESI-QTRAP-MS/MS (SCIEX, Carlsbad, CA, USA) [42].

2.4. Identification and Quantitative Analysis of Metabolites

We used the predetermined multiple reaction monitoring (MRM) method [42,43]. Analyst 1.6.3 (https://sciex.com/support/software-support/software-downloads, accessed on 11 February 2023) was used to convert and analyze MRM signals. Metabolites were quantified and identified using the Metabolite Database (Metware Biotechnology, Wuhan, China). The principal component analysis (PCA) used the statistical function prcomp of R, the data were unit variance scaled before PCA. VIP scores were obtained using the orthogonal projections to latent structures discriminant analysis (OPLS-DA) in the R package MetaboAnalystR 4.0 (https://metaboanalyst.ca, accessed on 11 February 2023) [44]. The differentially accumulated metabolites (DAMs) were identified using the following screening criteria: VIP score ≥ 1, absolute log2 (fold change) ≥ 1 and p value < 0.05.

2.5. RNA Extraction and Sequencing

The samples (0.1 g each) were ground into powder using liquid nitrogen, and RNA was extracted with the TransZol RNA Kit (TransGen Bioth, Beijing, China). The quality and quantity of total RNA were determined using NanoDrop ND1000 (Nanodrop Technologies, Wilmington, DE, USA). RNA integrity was determined by RNase-free agarose gel electrophoresis and the Agilent 2100 Bioanalyzer (Thermo Fisher Scientific, Waltham, MA, USA). The mRNA was purified by magnetic beads with Oligo (dT) attached. SuperScript™ II Reverse Transcriptase (Invitrogen, Carlsbad, CA, USA) was used to synthesize cDNA and the sequencing adaptor was linked to both ends to obtain the final cDNA library. The cDNA library was sequenced on the Illumina sequencing platform NovaSeq 6000 (Illumina, San Diego, CA, USA) with the assistance of Metware Biotechnology (Wuhan, China).

2.6. RNA-seq Data Analysis and Annotation

Fastp v0.19.3 [45] was used to filter the original reads to obtain clean reads. HisAT v2.1.0 [46] software aligned clean reads to the SB genome (https://bigd.big.ac.cn/gwh/Assembly/10400/show, accessed on 10 December 2023) to obtain position and specific sequence characteristics information. New genes were predicted with StringTie v1.3.4d [47]. The FPKM values of each gene representing expression were determined with Feature Counts v1.6.2 [48]. All transcripts were annotated from the Pfam, trEMBL, Swiss-Prot, NCBI non-redundant (Nr), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. DESeq2 v1.22.1 [49] was used to analyze differential expression. The genes featuring a corrected absolute log2 (fold change) ≥ 1 and p value < 0.05 were considered as DEGs. The number and type of TFs in all samples were annotated from the iTAK (https://github.com/kentnf/iTAK, accessed on 15 December 2023) database.

2.7. Weighted Gene Co-Expression Network Analysis

The co-expression network analysis of DEGs and DAFs (differentially accumulated flavonoids) was performed using online software (https://cloud.metware.cn, accessed on 15 December 2023). Modules were obtained through the WGCNA tool in R (default settings). The Inter-omics correlation network tools and the Metware Cloud platform (https://cloud.metware.cn, accessed on 16 December 2023) were used to calculate the correlation between four DAFs and hub genes. Groups with absolute correlation coefficient values ≥ 0.9 between hub genes and DAFs were used to draw an interaction network diagram.

2.8. Quantitative Real-Time PCR Analysis

Total RNA isolation used the TransZol RNA Kit (TransGen Bioth, Beijing, China). TransScript Uni All-in-One (TransGen Bioth, Beijing, China) was used to obtain first-strand cDNA. PerfectStart Green qPCR SuperMix (TranGen Bioth, Beijing, China) was selected as the fluorochrome and the Real-Time PCR detection system (Thermo Fisher Scientific, Waltham, MA, USA) was used to perform RT-qPCR. The Primer 5.0 tool was used to design the primer pairs for RT-qPCR (Table S1). The 5s (NCBI Gene ID: 24573090) gene was used as the internal standard, and the 2−∆∆Ct comparative Ct method was used to calculate the results of the RT-qPCR.

2.9. Vector Construction and Genetic Transformation of SB Callus

We used the pCAMBIA1302-35S-GFP vector for genetic transformation. The Premier 5.0 tool was used to design the primer pairs for MYB8, MYB14 and bHLH94 gene sequences (Table S1), and PrimeSTAR® HS (Takara, Beijing, China) was used to obtain the target gene fragment by PCR. ClonExpress Ultra (Vazyme, Nanjing, China) was used for homologous recombination and transformation of the constructed plasmid into DH5α Chemically Competent cells (TransGen Bioth, Beijing, China). Positive colonies were randomly selected for sequencing (Sangon Biotech, Shanghai, China) to verify the sequences, and the Plasmid Mini Kit (Omega-biotek, Norcross, GA, USA) was used to extract plasmids. The plasmids were transformed into Agrobacterium GV3101 (Sangon Biotech, Shanghai, China) and positive colonies were selected for PCR verification. Colonies were inoculated into LB liquid medium and cultured to an OD600 value of 0.6. Cultures were centrifuged at 6000× g for 15 min (GL-20G-2, Anke, Shanghai, China) and a suspension (OD600 value of about 0.6) was prepared using a liquid MS medium containing 100 μM/L acetosyringone (Solarbio, Beijing, China). SB callus tissue was infected with the suspension for 20 min, after which the callus was transferred onto MS solid medium and cultured at 24 °C in a dark environment for 3 days. The callus tissue was washed with MS liquid culture medium containing 500 mg/L cephalosporin and transferred to MS solid culture medium containing hygromycin (20 mg/L) and cephalosporin (500 mg/L). After two weeks of incubation at 24 °C, under a light/dark cycle of 16 and 8 h, subsequent experiments were performed.

2.10. Measurement of Total Flavonoid Content

Four wet calluses were ground into powder in liquid nitrogen, and total flavonoid content was determined using approximately 0.3 g of powder [50,51,52,53]. The powder was extracted with 10 mL of 70% (v/v) ethanol in an ultrasound bath at 40 °C for 1 h, followed by centrifugation. A 5% (w/v) sodium nitrite solution (300 µL) was added to 1 mL of supernatant and incubated for 6 min. This was followed by the addition of 0.3 mL of 5% (w/v) aluminum nitrate and 6 min incubation. Finally, 4 mL of 4% (w/v) sodium hydroxide was added and the mixture was left to react for 12 min. A 70% (v/v) ethanol solution was added to reach a total volume of 25 mL. Absorbance at 510 nm was measured in a microplate reader (Multiskan GO 1510, Thermo Scientific, Waltham, MA, USA). A blank solution was obtained following the same protocol but without any powder. The flavonoid rutin (quercetin-3-O-rhamnosylglucoside) was used to generate a standard curve (y = 40.508 x + 0.0961, where y is the absorbance and x is the concentration of the sample; R2 = 0.9988).

2.11. Statistical Analysis

All data were displayed as mean ± standard deviation (SD). SPSS 26.0 software (https://www.ibm.com/support/pages/spss-statistics-260-fix-pack-1, accessed on 22 January 2023) was used for statistical analyses, and ANOVA was used to test the significance of the differences between groups.

3. Results

3.1. Metabolite Analysis of SB Tissues

PCA analysis of the metabolite profiles of the six SB tissues shown in Figure S1 shows that the six tissues clustered independently, although root and mature seeds are located farther from the four other aboveground tissues (Figure 1a). The correlation analysis heat map for the fifteen pairwise comparisons shows some correlation between immature seeds with stem and mature seeds, but roots are not correlated with other tissues (Figure 1b). The clustered heat map for the 947 metabolites identified (see Table S2) supports this conclusion, although the pattern appears quite distinct for each tissue (Figure 1c). The metabolites in Table S2 include 379 flavonoids, 158 phenolic acids, 145 terpenoids, 80 alkaloids, 58 lignans and coumarins, 22 quinones, 7 tannins and 98 other metabolites. We identified tissue-specific metabolites in root (28), stem (4), leaf (5), petal (1) and immature seed (2). The 28 tissue-specific model metabolites in the root included 27 phenolic acids and 1 terpenoid. The four tissue-specific metabolites in stem were soyasaponin VI, ceanothic acid-3-O-glucuronic acid-rutinose, uralsaponin R and vnilloylcaffeoyltartaric acid. In the leaf, accumulation was observed for azukisaponin IV, bayogenin-3-O-glucuronide-28-O-glucoside, hydroxysoyasaponin I glucuronic acid glucose rhamnoside, genipin and calycosin-7-O-glucoside. Finally, petals accumulated glyyunnansapogenin I, whereas daphnin and squasapogenol accumulated in immature seed. No metabolites were observed to specifically accumulate in mature seeds.
In roots, the contents of flavones, isoflavones and chalcones in roots was higher than those in aboveground tissues. For example, we observed a higher content of chrysosplenetin, nevadensin, baicalein, naringenin, 7,3′,4′-trihydroxyquercetin, pinostrobin chalcone, isomedicarpin, thymonin and sophoricoside (see Table S2).
The DAFs in the fifteen pairwise comparisons were analyzed according to the Variable Importance in Projection (VIP) score ≥ 1, absolute log2 (fold change) ≥ 1 and p value < 0.05 (Table S2). There were 373 DAFs among all groups (see classification, relative content and annotation in Table S3). In most comparisons, mature and immature seeds showed less differentially expressed metabolites than other tissues (Figure 2a). A heat map shows high tissue variability in the relative content of these DAFs (Figure 2b and Table S3). For example, roots contain almost no flavanols, but they show the highest content of flavones, chalcones and isoflavones. In contrast, petals and seeds were higher in anthocyanin than other tissues, whereas in mature seeds, content of flavonols and flavanonols was the highest in seeds. Comparing root with every other tissue, there were 147 DAFs found in all comparisons (Figure 2c and Table S4), which included baicalin, baicalein, scutellarein and wogonoside. As expected, the root showed the highest content of baicalin and baicalein, and the lowest in wogonoside and scutellarein, which were predominantly found in immature seeds and in petals, respectively (Figure 2d and Table S4); however, this was in contrast to other reports [21,41]. In addition, we also identified medicinal flavonoids in aboveground tissues, such as cyanidin-3-O-glucoside, quercetin-3-O-glucuronide, naringenin, 7-O-methylluteolin, rivularin, chrysin and apigenin. Therefore, these results provide a basis for the rational use of the aboveground tissues of SB.

3.2. Overview of the Transcriptome Data

To investigate the genes involved in flavonoid synthesis in SB, we performed transcriptome sequencing. The three replicates of the six tissue samples were used to establish eighteen libraries. Each tissue sample was sequenced to obtain between 68,801,886 and 111,091,776 raw reads. The clean reads accounted for between 85.26% and 92.96% of the raw data. The quality control parameters were set at Q20 ≥ 97.76% and Q30 ≥ 92.04% for each library. The GC content of each library was between 47.48% and 48.67%. The mapping ratio of clean reads ranged from 87.19% to 94.76%. Compared with the reference genome, at least 83.72% of clean reads were aligned to exons (Table S5). Collectively, expression information was obtained for 32,857 genes, of which 4921 were novel genes. These genes were compared to various annotation databases. The number of annotated genes in these databases were 22,583 (KEGG) 23,143 (SwissProt) 30,235 (GenBank Non-redundant (Nr)), 16,592 (KOG), 30,096 (Trembl), 26,208 (Gene Ontology (GO)) and 25,356 (Pfam) (Table S6). Sample correlation analysis (Figure 3a) and PCA (Figure 3b) showed similarity in replicates (intra-sample repeats) and also significant inter-sample differences, especially in root, leaf and petal. Expression was similar in the replicates, as shown in the heat map (Figure 3c) indicating a high quality of the sequencing reads and suitability for further analysis.

3.3. Identification of DEGs

From the 15 comparisons, based on read counts of the transcripts obtained from the transcriptome data, a total of 25,385 DEGs were obtained (Table S7). In the comparison groups, the number of down- and up-regulated genes was similar (Figure 4a). Comparing root versus each overground tissue, there were 2121 common DEGs (Figure 4b), which may regulate differential flavonoid accumulation in root relative to aboveground tissues.
DEGs were classified into three categories in the GO classification analysis (Figure 4c and Table S7). In the Cellular Component, most DEGs were mapped to organelle terms, cell and cell part. In the Biological Process, nearly all DEGs were mapped to cellular and metabolic processes. For Molecular Function, more than 85% of DEGs were involved in binding and catalytic activity. KEGG classification analysis annotated 8914 DEGs found in all samples into 146 pathways, mainly enriched for the biosynthesis of secondary metabolites, metabolic, plant–pathogen interaction and plant hormone signal transduction pathways (Figure 4d).
Comparing the six tissues, 317 DEGs were identified as related to flavonoid biosynthesis pathways (see details in Table S8), where 56 were expressed in all six tissues (Figure 5a and Table S8) and a number of genes were highly expressed in the root (6 CHS, 3 CHI, 3 4CL, 2 PAL and 1 FLS). Interestingly, most of these genes reduced their expression with the development of seeds, especially ANS, 4CL, F3′5′H, F3H and 3GT. By searching the iTAK database, 1520 DEG were identified as TFs (Table S9) and mainly included MYB, WRKY, NAC, C2H2, AP2/ERF and bHLH family members (Figure 5b). We verified by RT-qPCR nine randomly selected DEGs of the flavonoid synthesis pathway, which correlated well with RNA-sequencing data expression patterns (Figure 6).

3.4. Analysis of a Gene Co-Expression Regulatory Network

The WGCNA analysis was conducted using four DAFs: baicalin, baicalein, scutellarein and wogonoside. The FPKM values of 25,385 DEGs were identified from the 18 SB transcriptome libraries. Comparison of gene expression resulted in a cluster dendrogram (Figure 7a) of 13 modules, although one (grey) was unreliable. Modules MEturquoise and MEblue contained the highest number of genes (7138 and 4974, respectively), whereas MEtan contained the lowest. Correlation was high between MEturquoise, MEpurple, MEmagenta and MEred (Figure 7b). A correlation heat map between the 13 modules and these four compounds (Figure 7c) showed positive correlation between the MEturquoise module and baicalin and baicalein, whereas MEtan module correlated with wogonoside and scutellarein. The latter was also highly correlated with MEgreen and MEred modules (0.97 and −0.82, respectively). Gene expression levels of MEgreen, MEred and MEturquoise were consistent with the accumulation of the four compounds in plant tissues (Figure 7d).
The correlation between the expression of hub genes in modules MEgreen, MEtan and MEturquoise and the four compounds was calculated. Fourteen structural genes were highly correlated (absolute cor ≥ 0.8) with baicalin, baicalein and scutellarein (Table S10). In addition, we identified 241 TFs associated to these four compounds (absolute cor ≥ 0.8), which included 25 MYBs and 15 bHLHs (Table S10). The 13 highly correlated structural genes (absolute cor ≥ 0.9) and 21 highly correlated TFs (absolute cor ≥ 0.9) were used to construct correlation networks (Figure 8). For example, three genes, SbMYB8 (Sb02g25620) (cor > 0.97), SbMYB14 (Sb09g00160) (cor > 0.98) and SbbHLH94 (Sb07g11990) (cor > 0.97), were highly correlated with flavonoid biosynthesis genes.

3.5. Overexpression of SbMYB8/SbMYB14/SbbHLH94 Promotes the Synthesis of Flavonoids in SB

We used the GV3101 Agrobacterium-based genetic transformation system to overexpress in SB calluses the three genes referred to above, to obtain overexpression (OE) calluses (myb8-OE, myb14-OE and bhlh94-OE) (Figure 9a), whereas SB callus containing an empty vector was used as a negative control (C). The flavonoid content in the three sample wet calluses was significantly higher than in the control, with a total flavonoid content of 0.17 mg/g (myb8-OE), 0.28 mg/g (myb14-OE) and 0.20 mg/g (bhlh94-OE) (Figure 9b). Expression levels of SbMYB8, SbMYB14 and SbbHLH94 in the respective overexpression calluses were also higher than in C (Figure 9c), and expression of key structural genes (PAL2, 4CL, CHS2 and CHI) was strongly dependent on the OE callus sample (Figure 9d), suggesting a link between these TF genes and flavonoid synthesis.

4. Discussion

SB is known to produce a number of natural products of interest [24]. The root contains flavonoid metabolites such as baicalin, baicalein and wogonoside, which are the main evaluation indices for medicinal quality in the Chinese Pharmacopoeia. Flavonoids are important for plant physiology and have pharmacological activity as anti-inflammatory agents and antioxidants, preventing cardiovascular diseases and aging, used in cancer treatment or as hepatoprotectives [54].
Herein, we used a metabolomics approach to analyze the metabolite accumulation in six tissues of SB. Among the six tissues, the number of flavonoids in roots was the least, with only 328 members detected (Table S2). However, the relative content of total flavonoids in roots was the highest, followed by mature seeds. Among the six tissues, roots contained more baicalin, baicalein, rehderianin I, norwogonin and dihydrobaicalein. For example, baicalin was 523 times more abundant in roots than in stem, and 1154 times more abundant than in mature seed, whereas baicalin was not detected in leaf, petal or immature seed.
Aboveground tissues contained secondary metabolites not found in the root, with antibacterial, anti-inflammatory and antioxidant activities such as isoscutellarein, luteolin, dihydroquercetin, quercetin-3-O-glucuronide, isorhamnetin-3,7-O-diglucoside, cyanidin-3-O-galactoside, kaempferol-3-O-sophoroside and kaempferol-3-O-sulfonate. The latter was only found in stem, leaf and mature seed. Consistent with previous reports [25,55,56], we show that DAMs were enriched in biosynthesis pathways of isoflavone, phenylalanine, flavonoid, flavonol and anthocyanin. Mature seeds contain abundant secondary metabolites such as anthocyanin, dihydroflavonol and flavonol, and have a darker color than immature ones. Anthocyanin might be a key factor in the color development of mature seeds [57], consistent with its higher content compared to flowers.
However, the distribution of flavonoids between root and other tissues was markedly different; for example, in contrast to previous studies [21,41], wogonoside was not found in the roots but accumulated in petals and immature seeds, whereas norwogonin was more abundant in the root than in other tissues. These differences may be due to the use of different varieties of SB, but we consider this unlikely because in this case, differences between varieties would be larger than between species SB and S. barbata [21]. Other factors may contribute to these differences that may be related to the age of the plants: 2-year-old plants in [41], not explicitly stated in [21], and 3-year-old plants herein. Alternatively, they could be due to differences in the extraction protocols. These factors require future exploration.
In our study, transcriptome analysis identified 56 key genes from the flavonoid synthesis pathway in all six SB plant tissues, and expression levels of structural genes were significantly higher in roots. The accumulation of baicalin and baicalein positively correlated with these gene expression profiles, where 4CL showed higher expression in roots and immature seeds. Both 4CL and C4H encode products upstream in the flavonoid biosynthesis pathway, and their overexpression in SB roots increases flavonoid content [58]. The majority of PAL, CHS and CHI genes were more expressed in root, which correlated (cor > 0.95) with baicalin accumulation. Six CHS family genes were identified in the MEturquoise module, with similar expression patterns. In petals and immature seeds, expression of FLS (Sb05g11050) (encoding flavonol synthase) was higher than in other tissues, and the expression pattern was consistent with the trend in scutellarein (cor > 0.94) accumulation.
In addition, we found that 74 of the 4921 new genes were annotated to the flavonoid biosynthesis pathway, including flavonoid O-methyltransferase, Flavanone 3-dioxygenase, flavone 3′-O-methyltransferase, anthocyanidin 3-O-glucoside 6″-O-acyltransferase and Coumaroyl-CoA. These genes may be involved in the flavonoid biosynthesis pathway of SB, which needs further study. Out of the DEGs, 46 novel genes were annotated to the flavonoid biosynthesis pathway, including four F3H (novel. 2845, novel. 2823, novel. 2822 and novel. 2846), two CHI (novel. 2636 and novel. 1922) and two C4H (novel. 3448 and novel. 3447), which enrich the gene database in the SB of flavonoid biosynthesis pathway.
The expression levels of DEGs in the flavonoid synthesis pathways were related to differences in contents and types of flavonoids in different tissues. We constructed a regulatory network between DAFs and DEGs by WGCNA analysis, which showed that 241 TFs were highly correlated with baicalin, baicalein and scutellarein accumulation (cor ≥ 0.8), whereas 25 MYB TFs and 15 bHLH were differentially expressed in SB tissues. MYBs are important for flavonoid production [59,60,61,62]. In our study, overexpression of SbMYB8, SbMYB14 and SbbHLH94 in SB callus led to more flavonoid production and higher expression of structural genes involved in flavonoid synthesis, suggesting that these three genes are involved in the production of flavonoids.
Recently, a similar study has applied multi-omics to investigate the flavonoid biosynthesis pathways in SB [41]. Like the present work, that study used tissues of both aerial and root origin, but not immature and mature seeds, although a more detailed analysis of roots was performed, with samples corresponding to xylem, phloem and periderm. In contrast to the present paper, only a handful of metabolites (baicalein, scutellarein, norwogonin, wogonin, and their glycosides (baicalin, scutellarin, norwogonoside and wogonoside) were analyzed, and work was focused on the characterization of the two flavonoid biosyhthetic pathways with detailed proteomic information, providing data on phosphorylation of various enzymes. In particular, MYB8 was not found to be differentially expressed versus the control in any of the tissues examined, whereas MYB14 was more expressed in leaves and stem, but showed lower expression in xylem. In contrast, we found that both TFs enhanced flavonoid production, which supports that transcription and translation are not necessarily linked. Therefore, in a way, the two papers can be considered as complementary.

5. Conclusions

Metabolomics and transcriptomics were used to explore the differences in metabolites between six tissues of SB, to determine genes and differences in flavonoid biosynthesis among different SB tissues. Finally, the regulation of SbMYB8, SbMYB14 and SbbHLH94 on the flavonoid biosynthesis pathway of SB was verified. Roots accumulated more baicalin and baicalein than aboveground tissues, but they were low in scutellarein and wogonoside, in contrast to previous reports. These differences may be attributed to either plant variety, age of the plants or the extraction protocol, which should be explored in future work.
Transcriptome analysis identified 34 genes highly associated with baicalin, baicalein and scutellarein, 13 of which were key structural and 21 genes encoded TFs. Overexpression of SbMYB8, SbMYB14 and SbbHLH94 increased total flavonoid content in SB callus and flavonoid synthesis structural gene expression levels (Figure 10).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14071494/s1, Figure S1: The six plant tissues. Figure S2: The total ion current (TIC) and MRM metabolite detection multi-peak map (multi-material extraction ion current spectrum, XIC) of mixed quality control QC samples. The abscissa is the retention time (retention time, Rt) of metabolite detection, and the ordinate is the ion flow intensity of ion detection (intensity unit is cps, count per second). N represents negative ion mode; P represents positive ion mode. Figure S3: The multi-peak diagram of MRM metabolite detection in the multi-reaction monitoring mode shows the substances that can be detected in the sample. Each chromatographic peak of different colors represents a metabolite detected. N represents negative ion mode; P represents positive ion mode. Figure S4: The quantitative analysis integral correction results of randomly selected metabolites in different samples. The abscissa is the retention time (min) of metabolite detection and the ordinate is the ion current intensity (cps) of a metabolite ion detection. N represents negative ion mode; P represents positive ion mode. Table S1: The primer pairs used in the study. Table S2: List of 947 accumulated metabolites identified. Table S3: List of the 373 differentially accumulated flavonoid. Table S4: List of the 147 overlapping DAFs. Table S5: Sequencing output statistics of the 15 samples. Table S6: All genes annotated based on GO, KEGG, Nr, KOG, Pfam, SwissProt and Trembl databases. Table S7: The 25,385 DEGs identified. Table S8: The 317 DEGs of the flavonoid biosynthesis pathway. Table S9: A total of 1520 transcription factors were identified in the DEGs by searching the iTAK database. Table S10: The correlation value of hub genes in the MEgreen, MEtan and MEturquoise modules with baicalin, baicalein, wogonoside and scutellarein.

Author Contributions

Conceptualization, Y.N.; data curation, Z.L.; formal analysis, D.Y.; funding acquisition, D.W.; investigation, Y.H.; methodology, D.W.; project administration, D.W.; resources, B.C. and W.Y.; supervision, Y.N.; validation, D.Y., J.X. and Q.T.; visualization, J.W.; writing—original draft, D.Y.; writing—review and editing, D.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of Shanxi Province of China, grant number No. 20210302123376, Supported by the earmarked fund for CARS, grant number No. CARS-21, Earmarked Fund for Modern Agroindustry Technology Research System, grant number No. 2024 and Shanxi Research Innovation Project for Postgraduate Students, grant number No. 2023KY320.

Data Availability Statement

The data presented in this study are openly available in [SRA] at (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1079421, accessed on 4 June 2024), reference number [PRJNA1079421].

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Metabolite profiles of six tissues of SB and their replicates. (a) PCA plot of metabolites; (b) correlation analysis heatmap between the 18 samples (six tissues and their three replicates), where correlation is color coded (see right legend); (c) clustered heatmap analysis of 947 metabolites relative content and the SB samples indicated, where content is color coded and normalized by Z-score.
Figure 1. Metabolite profiles of six tissues of SB and their replicates. (a) PCA plot of metabolites; (b) correlation analysis heatmap between the 18 samples (six tissues and their three replicates), where correlation is color coded (see right legend); (c) clustered heatmap analysis of 947 metabolites relative content and the SB samples indicated, where content is color coded and normalized by Z-score.
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Figure 2. Profiles of DAFs for six tissues of SB and replicates. (a) The number of DAFs in fifteen pairwise comparisons; (b) heatmap of 373 DAFs by relative content, where DAFs content is color coded and normalized by Z-score (see right legend, upper); (c) Venn diagram of roots compared to each aboveground tissue; (d) clustered heatmap analysis of the four compounds indicated in six SB tissues and their replicates, where metabolite content is color coded and normalized by Z-score.
Figure 2. Profiles of DAFs for six tissues of SB and replicates. (a) The number of DAFs in fifteen pairwise comparisons; (b) heatmap of 373 DAFs by relative content, where DAFs content is color coded and normalized by Z-score (see right legend, upper); (c) Venn diagram of roots compared to each aboveground tissue; (d) clustered heatmap analysis of the four compounds indicated in six SB tissues and their replicates, where metabolite content is color coded and normalized by Z-score.
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Figure 3. Transcriptome data analysis of SB six tissues and their replicates. (a) Sample correlation analysis based on transcriptome data; (b) PCA plot of all identified genes; (c) heatmap analysis where content of genes is color coded and normalized by Z-score (see right legend).
Figure 3. Transcriptome data analysis of SB six tissues and their replicates. (a) Sample correlation analysis based on transcriptome data; (b) PCA plot of all identified genes; (c) heatmap analysis where content of genes is color coded and normalized by Z-score (see right legend).
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Figure 4. Identification of DEGs in six SB tissues. (a) Number of DEGs in the 15 pairwise comparisons; (b) Venn diagram comparing roots and aboveground tissues; (c,d) KEGG classification histogram (c) and GO classification histogram (d) for DEGs in 15 comparative groups.
Figure 4. Identification of DEGs in six SB tissues. (a) Number of DEGs in the 15 pairwise comparisons; (b) Venn diagram comparing roots and aboveground tissues; (c,d) KEGG classification histogram (c) and GO classification histogram (d) for DEGs in 15 comparative groups.
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Figure 5. Identification of DEGs in the flavonoid synthesis pathway. (a) Expression patterns of genes involved in the flavonoid synthesis pathway (vertical column) in each of the 18 SB samples. The content of genes is color coded and normalized by Z-score (right legend, upper); (b) top 15 TF families identified within the DEGs. The number above the bar graph represents the number of individual genes in the TF families.
Figure 5. Identification of DEGs in the flavonoid synthesis pathway. (a) Expression patterns of genes involved in the flavonoid synthesis pathway (vertical column) in each of the 18 SB samples. The content of genes is color coded and normalized by Z-score (right legend, upper); (b) top 15 TF families identified within the DEGs. The number above the bar graph represents the number of individual genes in the TF families.
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Figure 6. Expression of nine DEGs (eight known and one novel) in the six SB samples using RNA-seq (bars, left) or RT-qPCR (lines, right). RT-qPCR results were calculated by the 2−ΔΔCt comparative method.
Figure 6. Expression of nine DEGs (eight known and one novel) in the six SB samples using RNA-seq (bars, left) or RT-qPCR (lines, right). RT-qPCR results were calculated by the 2−ΔΔCt comparative method.
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Figure 7. WGCNA of DEGs identified in six tissues of SB. (a) Clustering of DEGs into 13 modules; (b) inter-branch correlation coefficients; (c) correlation between modules and the four compounds (module-trait correlations according to right-scale), where numbers in parentheses are p values and modules are color coded; (d) gene expression levels in the modules indicated for each of the experimental samples (upper) and common expression pattern (module eigengene E) of co-expression genes in the module (lower).
Figure 7. WGCNA of DEGs identified in six tissues of SB. (a) Clustering of DEGs into 13 modules; (b) inter-branch correlation coefficients; (c) correlation between modules and the four compounds (module-trait correlations according to right-scale), where numbers in parentheses are p values and modules are color coded; (d) gene expression levels in the modules indicated for each of the experimental samples (upper) and common expression pattern (module eigengene E) of co-expression genes in the module (lower).
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Figure 8. Correlation network diagram between DEGs and flavonoids. Orange circles: DEGs of the flavonoid synthesis pathways, blocks: TFs, red: MYB family, green: bHLH family, purple rhombi: metabolites, MWS0052: baicalin, MWSHY0023: baicalein, MWSslk200: scutellarein.
Figure 8. Correlation network diagram between DEGs and flavonoids. Orange circles: DEGs of the flavonoid synthesis pathways, blocks: TFs, red: MYB family, green: bHLH family, purple rhombi: metabolites, MWS0052: baicalin, MWSHY0023: baicalein, MWSslk200: scutellarein.
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Figure 9. Total flavonoid content and gene expression analysis in SB overexpressed calluses. (a) GV3101-induced SB callus (see text for definitions); (b) total flavonoid in the four callus indicated; (c) expression levels of TFs myb8, myb14 and bhlh94 in the four samples indicated; (d) expression levels of key genes involved in flavonoid synthesis pathways in the four samples indicated, where * p < 0.05; ** p < 0.01 and *** p < 0.001.
Figure 9. Total flavonoid content and gene expression analysis in SB overexpressed calluses. (a) GV3101-induced SB callus (see text for definitions); (b) total flavonoid in the four callus indicated; (c) expression levels of TFs myb8, myb14 and bhlh94 in the four samples indicated; (d) expression levels of key genes involved in flavonoid synthesis pathways in the four samples indicated, where * p < 0.05; ** p < 0.01 and *** p < 0.001.
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Figure 10. TF regulation of SB flavonoids biosynthesis pathways. Regulatory relationships are uncertain (blue arrows) and DEGs identified in the transcriptome (red).
Figure 10. TF regulation of SB flavonoids biosynthesis pathways. Regulatory relationships are uncertain (blue arrows) and DEGs identified in the transcriptome (red).
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MDPI and ACS Style

Yao, D.; Xing, J.; Tang, Q.; Hou, Y.; Chen, B.; Yao, W.; Li, Z.; Wang, J.; Niu, Y.; Wang, D. Flavonoid Biosynthesis in Scutellaria baicalensis Georgi: Metabolomics and Transcriptomics Analysis. Agronomy 2024, 14, 1494. https://doi.org/10.3390/agronomy14071494

AMA Style

Yao D, Xing J, Tang Q, Hou Y, Chen B, Yao W, Li Z, Wang J, Niu Y, Wang D. Flavonoid Biosynthesis in Scutellaria baicalensis Georgi: Metabolomics and Transcriptomics Analysis. Agronomy. 2024; 14(7):1494. https://doi.org/10.3390/agronomy14071494

Chicago/Turabian Style

Yao, Dongzuo, Jiaqian Xing, Qingye Tang, Yue Hou, Binbin Chen, Wenmiao Yao, Zhenfang Li, Jiaxing Wang, Yanbing Niu, and Defu Wang. 2024. "Flavonoid Biosynthesis in Scutellaria baicalensis Georgi: Metabolomics and Transcriptomics Analysis" Agronomy 14, no. 7: 1494. https://doi.org/10.3390/agronomy14071494

APA Style

Yao, D., Xing, J., Tang, Q., Hou, Y., Chen, B., Yao, W., Li, Z., Wang, J., Niu, Y., & Wang, D. (2024). Flavonoid Biosynthesis in Scutellaria baicalensis Georgi: Metabolomics and Transcriptomics Analysis. Agronomy, 14(7), 1494. https://doi.org/10.3390/agronomy14071494

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