Identification of SbWRKY Transcription Factors in Scutellaria baicalensis Georgi under Drought Stress and Their Relationship with Baicalin

Abstract

One of the most significant transcription factors in plants, WRKYs, are crucial for plant growth and stress response. In this study, we analyzed the physicochemical properties, evolutionary relationships, conservation structure, and expression of the WRKY gene family in S. baicalensis. The WRKY family has highly conserved structural domains, which have been classified into three major categories, I, II, and III, based on the number of WRKY structural domains and zinc finger structural features. SbWRKYs of the same subgroup are functionally similar and essentially contain the same motif. Additionally, different drought stress situations resulted in varying levels of SbWRKYs expression, with the majority of these factors being up-regulated in moderate drought stress settings, and fewer of them were up-regulated under severe drought stress conditions. Under moderate drought stress, the expression of key enzymes increased, while under severe drought stress, the expression of key enzymes decreased. Mild drought stress resulted in a 26.42% increase in baicalin accumulation, while severe drought stress led to a 22.88% decrease. The protein interaction analysis of key enzyme genes and SbWRKYs revealed that the expression of key enzyme genes affected the expression of SbWRKYs. We screened nine SbWRKYs with a significant relationship with baicalin accumulation, and SbWRKY8 and SbWRKY16 showed the highest correlation with the baicalin content. These findings offer a theoretical framework for more research on the roles of SbWRKYs and show that SbWRKYs can respond to drought stress in S. baicalensis.

1. Introduction

Scutellaria baicalensis Georgi is the dried root of Scutellaria baicalensis, a plant in the family Labiatae, which is a popular herbal medicine used in Chinese medicine to treat heat and dry dampness ailments [1,2,3]. Most of the active ingredients in S. baicalensis are flavonoid secondary metabolites, such as baicalin, baicalein, and wogonin, etc. Baicalin is used as the only substance for content determination in the Chinese Pharmacopoeia, indicating that its content directly affects the quality of Scutellaria baicalensis Georgi [4,5]. It has been shown that moderate drought stress conditions can enhance the synthesis and accumulation of secondary metabolites in phytogenic herbs [6,7], and with moderate drought stress conditions, baicalin initiates secondary metabolic pathways and promotes the accumulation of flavonoids [8,9]. The main synthetic pathways and key enzyme genes of baicalin are shown in Figure 1 [10,11]. The classical flavonoid synthesis pathway begins with phenylalanine, which is metabolized via the shikimate pathway. Phenylalanine is catalyzed by phenylalanine ammonia lyase (PAL) to form cinnamic acid. Cinnamic acid is then catalyzed by cinnamate 4-hydroxylase (C4H) to form p-coumaric acid. This is catalyzed by 4-coumaroyl: CoA-ligase (4CL) to produce p-coumaroyl CoA, which, together with malonyl coenzyme A, produce naringenin chalcone under the action of chalcone synthase (CHS). Naringin is converted to chalcone, and then to naringin via the catalysis of chalcone isomerase (CHI). Ultimately, naringenin is catalyzed by flavone synthase (FNS) to produce flavonoids. The changes in the expression of key enzymes under different drought stress conditions could visually reflect the role of different key enzymes in baicalin synthesis and the correlation between their expression and the baicalin content.

The traditional single-gene point-based approach cannot accurately and comprehensively elaborate the secondary metabolic processes, while the study of histology offers the possibility of studying the secondary metabolic regulatory network of medicinal plants [12]. Transcriptomics, as a discipline that studies gene function and gene expression at a holistic level and explains the molecular mechanisms of specific biological processes, is an important element of functional genomic research [13,14]. So far, there have been more transcriptomics studies on plants, mainly focusing on economic crops and model crops, such as Oryza sativa L. [15], Zea mays L. [16], Hordeum vulgare L. [17], and Arabidopsis thaliana [18], and relatively few transcriptomics studies on medicinal plants to investigate the effects of abiotic stress on the content of secondary metabolites in medicinal plants. Secondary metabolites are the main components of medicinal plants that exert clinical effects, and the content of secondary metabolites is related to the quality of the medicinal plants [19]. Transcriptomics research can provide a theoretical basis for improving the content of secondary metabolites in medicinal plants, improving the quality of artificially cultivated medicinal plants and improving the germplasm resources of medicinal plants from a microscopic point of view.

One of the most crucial transcription factors in plants is the WRKY transcription factor family [20,21,22], named for its highly conserved WRKY structural domain [23,24]. WRKY transcription factors (TFs) are characterized by a highly conserved WRKY structural domain (WRKYGQK) in the n-terminal region of the protein, which acts as a DNA-binding structural domain [25,26]. They can be classified according to the number of WRKY structural domains and the characteristics of their zinc finger-like motifs. WRKY TFs with two WRKY structural domains belong to group I, whereas WRKY TFs with one WRKY structural domain belong to group II. The difference between group III and group II WRKY proteins is the type of zinc finger motif; group II has C-X4-5-C-X22-23-H-X1-H, and group III has C-X7-C-X23-H-X1-C [27,28]. WRKY TFs play an important role in the process of development and defense, including seed germination, pollen development, hormone regulation, secondary metabolite biosynthesis, and so on [29,30], and can regulate plant stress resistance. WRKYs can also regulate the expression of stress-related genes under abiotic stresses such as drought, high-temperature, salt, and low-phosphorus stress to improve plants’ resistance. For example, Solanum lycopersicum L. has a good drought and salt tolerance level, and Arabidopsis thaliana overexpressing TaWRKY46 showed greater osmotic stress tolerance [31,32]. In S. baicalensis, WRKYs were discovered and analyzed by Caijuan Zhang et al. [33]. They discovered that these WRKYs were expressed in various plant tissues, including the roots, stems, leaves, and flowers, as well as in response to various abiotic stresses, indicating that WRKYs are responsive to both S. baicalensis plants and abiotic stresses.

In this study, WRKYs were identified in the genome of S. baicalensis, and phylogenetic analysis was conducted. A total of 12 RNA-seq data sets were selected for expression pattern analyses. Based on gene-specific expression patterns and the up-regulated expression patterns in response to drought stress conditions treatment, nine WRKYs identified in S. baicalensis (SbWRKY) genes were revealed as potentially involved in the regulation of baicalin biosynthesis.

2. Materials and Methods

2.1. Plant Material

S. baicalensis potted plants cultivated for two years were used as the test material for transcriptome analysis under the following conditions: soil depth: 28.00 cm; pot depth: 30.50 cm; rim diameter: 31.20 cm; and bottom diameter: 20 cm, with one S. baicalensis plant planted in each pot, and each treatment was repeated three times. The temperature of each pot was kept the same as the soil, and they were entirely submerged in the ground. The soil employed in the experiment was a sandy loam with a composition of 25.74 g/kg organic matter, 99.47 mg/kg alkaline dissolved nitrogen (ADN), 27.24 mg/kg accessible phosphorus (AP), and 136.78 mg/kg available potassium (K). The amount of potassium was 136.78 mg/kg. Throughout the experiment, a rain shelter was built by rolling the plastic sheet up on clear days and re-laying it at night and on wet days. The treatment group experienced a drought in its natural form, and no additional irrigation was carried out after a single instance of watering the saturated soil, whereas the control group was kept at a normal level and maintained with daily watering (CK). At d. 12, 16, and 22, when the water content of the soil was tested, it was found to be 80% (MiDS), 60% (MoDS), and 40% (SeDS) of that of the control group, respectively. Three biological replicates were set up for each of the three points that corresponded to the three points indicated above and were utilized as the transcriptome data sources.

2.2. Screening of WRKYs in S. baicalensis and Prediction of Their Properties

The genomic data of S. baicalensis were downloaded from the CNCB website (National Center for Biological Information CNCB), and in order to find WRKYs in S. baicalensis, the hidden Markov model (HMM) of the WRKY domain (PF03106) was used. A total of 60 WRKYs were surveyed. The putative WRKY protein structures were screened and validated using SMART online software, and the duplicated and WRKY domain-free proteins were removed and renamed as SbWRKY TFs. In addition, the ExPASy Proteomics Server online software (https://www.expasy.org (accessed on 15 April 2023)) was used to predict the identified WRKY proteins were subjected to isoelectric point (PI) and molecular weight (MW).

2.3. Constructing a Phylogenetic Tree of SbWRKYs

We downloaded the protein sequences of the Arabidopsis thaliana WRKY transcription factor family from TAIR (https://www.arabidopsis.org/index.jsp (accessed on 15 June 2023)). After aligning the SbWRKYs using Mafft software, we constructed a phylogenetic tree using Iqtree. The classification method of Arabidopsis thaliana was used, and neighbor joining (NJ) was used with 1000 replicate settings.

2.4. Analysis of Conserved Structural Domains of SbWRKYs

The conserved structural domains and zinc finger structures of grouped SbWRKYs were analyzed using Jalview software. The structural distribution of introns and exons of SbWRKYs was analyzed using GSDS 2.0 online software (http://gsds.gao-lab.org/index.php/ (accessed on 17 June 2023)). The conserved motifs of SbWRKYs were predicted using MEME Suite 5.2.0 (http://meme-suite.org/index.html/ (accessed on 15 June 2023)) online software with the parameter setting of maximum motif number of 10 and other parameters as default settings.

2.5. Analyzing the Response of S. baicalensis Roots to Different Drought Stress Conditions Based on RNA-Seq Results

Transcriptome data of S. baicalensis were obtained from the public database of the National Center for Biotechnology Information (NCBI) (accession number: PRJNA1008364, with three replicates per treatment). The method used aimed to calculate the transcript abundance of SbWRKYs through estimation according to the number of fragments per base in the exon model (FPKM) per million mapped reads. The expression levels of the SbWRKYs were analyzed under different drought stress conditions. The expression data were transformed into log2 (FPKM +0.01) values for differential expression analysis [34]. The obtained gene expression profiles were visualized as heatmaps using pheatmap software. For the transcriptome analysis of SbWRKYs in response to drought stress, differentially expressed genes (DEGs) were defined with a threshold of p < 0.05 and |log2 (fold-change) ≥ 1| [35].

2.6. Extracting RNA and Performing Real-Time Fluorescence Quantitative PCR of S. baicalensis

Primers for quantifying the expression of SbWRKYs were designed using the primer3 website (https://www.yeastgenome.org/primer3/ (accessed on 22 October 2022)) (Table S2) [36]. We extracted the total RNA from the S. baicalensis roots using the Rneasy Plant Mini kit and purified it using the Oligonucleotide mRNA Mini kit. The purity and concentration were tested with the nanophotometer P330. Using the Bioteke Super RT kit, we reverse-transcribed the RNA into cDNA using total RNA as the template and Oligo-DT as the primer. The cDNA was stored at −20 °C. We detected the gene expression levels of PAL, C4H, SBCHS-2, FNSII-2, and 9 different DGEs using 18SrRNA as an internal reference (see Table S2 for primer information). In a 20 μL reaction, we used 10 μL of 2× SYBR premix Ex TAQ as the primer, with an optimal primer amount of 0.8 μL and an optimal cDNA amount of 1.0 μL. The optimized PCR reaction program was as follows: pre-denaturation at 95 °C for 30 s, denaturation at 95 °C for 5 s, annealing at 55 °C for 30 s, extension at 72 °C for 40 s, for 40 cycles. Data were generated by averaging 3 independent replicates.

2.7. Extraction and Content Determination of Baicalin

We added 0.30 g of baicalin powder to 12 mL of 70% ethanol and extracted the mixture for 6 min at a constant temperature of 80 °C using a CEM Mars 5 Microwave Accelerated Reaction System (CEM Corporation, Charlotte, NC, USA). After filtering the sample, we added 25 mL of 70% ethanol to achieve the final volume. We filtered the solution using a 0.45 μm microporous membrane after introducing it to a 25 mL volumetric flask, which had been filled to the mark with 70% ethanol. We created a standard curve using baicalin standard samples. We used the high-performance liquid chromatography (HPLC) elution procedure described by Han et al. [37] to determine the baicalin content, which was calculated according to the standard curve equation y = 2626.76x − 96.06 (R² = 0.9997).

2.8. Data Processing

Expression levels were determined using the 2^−ΔΔCt method. Microsoft Excel 2010 was used to process the original data. SPSS 19.0 was used for one-way ANOVA and Pearson correlation statistical analysis. Origin2022 was used to graph the data.

3. Results

3.1. Phylogenetic Analysis of the SbWRKY Family

The ID of the WRKYs was searched on the Pfam website as PF03106, and 60 WRKYs were selected from the S. baicalensis genome after downloading the WRKYs structural domain and renamed as SbWRKYs. The basic information of these genes, including the protein length, protein molecular weight, and theoretical isoelectric point of the proteins, are shown in Table S1, and the protein length was between 138 and 752 bp, the protein molecular weight was between 16.1 Da and 81.08 Da, and the theoretical isoelectric point was between 4.53 and 10.44.

A rootless phylogenetic tree of 60 SbWRKYs was constructed using the maximum likelihood (ML) method (Figure 2) to classify the SbWRKYs and evolutionary relationships using the AtWRKYs classification as a reference. The 60 SbWRKYs were divided into three primary groups of groups I, II, and III and four subgroups of group II. Thirteen SbWRKYs containing two WRKY structural domains were clustered in Group Ⅰ. Group II contains 34 SbWRKYs with one WRKY structural domain and a zinc finger motif of C-X4-5-C-X23-H-X1-H. In addition, group II was divided into five subgroups based on the presence of different sequences on their zinc finger motifs. Subgroup IIa contains a CX5CPVKKK (L/V) Q motif, subgroup IIb contains a CX5CPVRKQVQ motif, subgroup IIc contains a CX4C motif, and subgroup IIe contains a CX5CPARK (Q/M) V (E/D) motif. Subgroup IIa contains five CX5CPVKKK(L/V) Q motif WRKY proteins, which are IIa, 9 IIb, 9 IIc, and 11 IIe. Eight C2HC WRKY structural domains and zinc finger motifs (C-X7-CX23-31-H-X1-C) were classified as being in group III.

3.2. Chromosomal Localization in the Promoter of SbWRKY Gene and Key Enzyme Genes

To determine the distribution of SbWRKYs throughout the genome, we searched the SbWRKY genome sequence and localized all 60 identified SbWRKYs to the corresponding chromosomes. As shown in Figure 3, the SbWRKYs and key enzyme genes are co-distributed on nine chromosomes and are unevenly distributed, with the number of SbWRKYs and key enzyme genes per chromosome independent of chromosome length, with chr1 being the longest and containing 12 SbWRKYs (18%). Chr3 was of medium length, and contained 15 SbWRKYs and three key enzyme genes, C4H, CHI, and FNS (27.3%), while the remaining three key enzyme genes were found on chr4, 5, and 7, respectively, and chromosome 6 contained only one SbWRKY (SbWRKY 41). The location of groups I, II, and III on the chromosomes was not directly related to the grouping, while group 3 was distributed on all the chromosomes.

3.3. Conserved Motifs and Gene Structure Analysis of SbWRKYs

To further analyze the structure of SbWRKYs, 10 conserved motifs of 60 SbWRKYs were predicted using MEME online software. As shown in Figure 4D, motif 1 and motif 4 are WRKYDNA structural motifs, and motif 2 is a zinc finger motif. The results showed that motif 1 and motif 4 were present in all 60 SbWRKYs, indicating that the WRKYs have a highly conserved structure. SbWRKY59, SbWRKY55, SbWRKY60, and SbWRKY41 contained only motif 4, and the remaining fifty-six SbWRKYs contained more than three conserved ones. Only SbWRKY56, SbWRKY59, SbWRKY55, SbWRKY60, SbWRKY38, and SbWRKY41 do not have motif 3, and the rest contain motif 3 (Figure 4A). And from Figure 4A, we can see that Group Ⅰ contains motif 4 and motif 9, Group Ⅱb contains motif 5 and motif 10, Group Ⅱe contains motif 7, and Group Ⅲ contains motif 2, which shows that the proteins of SbWRKYs are very conserved. The NCBI Batch CD-Search Tool was used to predict the structural domains of the SbWRKYs, and all the SbWRKYs contained WRKY or WRKY superfamily structural domains (Figure 4B), demonstrating that all the 60 SbWRKYs screened belong to the WRKYs family. Among them, only SbWRKY56, SbWRKY59, SbWRKY43, SbWRKY38, and SbWRKY60 contained only the WRKY superfamily, while all other transcription factors contained the WRKY structural domain. The further analysis of the structure of SbWRKYs using GSDS online software showed that most SbWRKYs contained three parts: a non-coding region, an intron region, and an exon region (Figure 4C). The number of CDs ranged from 1 to 7, with the highest number of SbWRKYs found with three CDs, accounting for 43.3%. The number of CDs was similar in the same class, and the structures were similar; the differences were obvious in different classifications. The total average number of SbWRKYs exons was four, the maximum number of exons was seven, which appeared in SbWRKY4 and SbWRKY2, and the minimum number of exons was one, which appeared in SbWRKY57 and SbWRKY1.

3.4. Analysis of Cis-Regulatory Elements of the SbWRKY Promoter

Cis-regulatory elements, which are normally confined to the target gene’s 5′ upstream (promoter) sequence, are binding sites on target genes for transcriptional control by transcription factors. In order to look at the cis-regulatory components of the stress response, we sent the 2000 bp area upstream of each SbWRKY translation start point to PlantCARE. Figure 5 displays a total of 22 stress response components, including TATC-box (cis-regulatory components involved in the gibberellin response), ACE (cis-regulatory components involved in the light response), W Box (WRKY TFs-specific binding region), LTR (cis-regulatory components involved in the low-temperature response), TCA elements (cis-regulatory components involved in the salicylic acid reaction), SARE (a cis-regulatory element involved in salicylic acid reaction), AuxRR-core (a cis-regulatory element that participates in the growth hormone reaction), G-box (a cis-regulatory element that participates in the light reaction), CGTCA motif (a cis-regulatory element that participates in the meja reaction), TGCA motif (a cis-regulatory element that participates in cis-regulatory element), TGACG motif (a cis-regulatory element that participates), Gibberellin response element gare motif, wound response element WUN motif, MYB binding site linked to drought induction, and MYB binding site linked to light response MRE motif. There is at least one cis-regulatory component linked to the stress response in all SbWRKYs. Among them, (212, 20%) were ABRE-acting elements, which were the most abundant among the 15 cis-elements, suggesting that SbWRKY TFs play a role in the ABA pathway.

3.5. Analysis of Cis-Acting Elements of Key Enzyme Genes of S. baicalensis

Six key enzyme genes for flavonoid synthesis were downloaded from NCBI, and the 2000 bp region upstream of the start site was submitted to PlantCARE. As can be seen in Figure 6, a total of 31 homeopathic elements larger than two were present in the six key enzyme genes of S. baicalensis, with TATA-box (one of the binding sites of RNA polymerase, 398) and CAAT-box (binding site of transcription factor CTF/NF-1, 224). WRKY proteins specifically bind to the (T) (T) TGAC (C/T) sequence in the DNA-binding region, a region known as the W box, which contains a TGAC conserved core sequence that is essential for the binding of WRKY transcription factors [38,39]. There are five W-box homeostatic elements in four enzyme genes (4CL, C4H, CHI, and FNSII-2). It is possible that the WRKYs can bind to these genes and regulate their expression. WRKYs may bind to key enzyme genes and regulate their expression. This could increase the content of baicalin.

3.6. SbWRKYs and Key Enzyme Genes Expression Analysis under Drought Stress

The expression trends of SbWRKYs and key enzyme genes under various drought stresses were calculated using the FPKM values of WRKYs expression in the transcriptome sequencing results of S. baicalensis, and a heat map of the expression of SbWRKY TFs and key enzyme genes was generated (Figure 7) [40]. Under mild drought stress, SbWRKY14, SbWRKY26, SbWRKY45, SbWRKY47, SbWRKY6, ScWRK7, SbWRKY13, SbWRKY49, and SbWRKY25 were more strongly expressed than they were in the CK group. Under mild drought stress, 24 SbWRKYs shown an increase (FPKM > 1) in expression level. Different SbWRKYs were highly expressed under various treatments, with 24 SbWRKYs showing an increase in expression level under moderate drought stress (FPKM > 1) compared to only 5 SbWRKYs showing an increase and 20 SbWRKYs declining under severe drought stress (FPKM < 1). With the exception of CHI, whose expression level was increased under extreme drought stress, all the major enzyme genes showed less expression under drought stress relative to those in the CK group. These findings suggested that the SbWRKYs and important enzyme genes were involved in the stress response to drought.

3.7. Effect of Drought Stress on the Expression of Key Enzyme Genes in S. baicalensis

The impact of various levels of drought stress on baicalein’s key enzymes is shown in Figure 8A. During mild drought stress, PAL had a significantly different effect on the experimental group compared to that of the CK group. The expression level of PAL was high in the experimental group, with a 6.98-fold increase compared to that of the CK group. During moderate drought stress, the CK group had a higher expression level than the experimental group did. During severe drought stress, there was no noticeable difference. In Figure 8B, the effect on C4H was only significantly different during mild drought stress. The experimental group had a significantly higher expression level than the CK group did, reaching 6.55-fold. There was no significant difference in the other drought stress conditions. In Figure 8C, there was a significant difference in the effect of CK on SbCHS-2 compared to that of the control group. The CK expression level was significantly higher in the test group than it was in the CK group, and there was also a significant difference between the control group and the control group during moderate drought stress. However, the CK expression level was lower in the experimental group, and there was no significant difference under extreme drought stress. In Figure 8D, the effect on FNSII-2 was fundamentally different from that of the control group under extreme drought stress. The CK expression level was higher than that in the treatment group, and the CK expression level during medium drought stress was higher than that in the treatment group, but there was no significant difference. This result is different from the expression of the key enzyme genes in the transcriptome data in Section 3.6, and the reason may be that the expression of the key enzyme genes leads to the change of the expression of the SbWRKYs, so the expression of the key enzyme genes in the transcriptome is reduced.

3.8. Effect of Drought Stress on the Content of Baicalin

As displayed in Figure 9, during mild drought stress, the baicalein content of the experimental group was significantly higher than that of the CK group by 1.26 times, and during severe drought stress, the baicalein content of the experimental group was significantly lower than that of the CK group by 0.77 times, which might be related to the expression of the key enzyme genes analyzed in 3.7. During severe drought stress, the expression level of key enzyme genes in the experimental group was lower than that in the CK group or there was no significant difference. During moderate drought stress, there was almost no difference in baicalin contents between the experimental group and the CK group.

3.9. Protein Interaction Network between SbWRKYs and Key Enzyme Genes

To analyze the interactions between SbWRKYs and key enzyme genes, 60 SbWRKYs and 6 key enzyme genes were analyzed and mapped using string (Figure 10), from which it can be seen that there are obvious interactions between 60 SbWRKYs. The important enzyme genes interact with SbWRKY16 strongly, as shown in Figure 10, and there are strong interactions between these six genes SbWRKY30, SbWRKY43, SbWRKY8, SbWRKY33, SbWRKY27, and SbWRKY44 and the key enzyme genes, with the exception of FNSII-2. It is likely that the 60 SbWRKYs interact with one another as they all have either close or reciprocal connections with one another.

3.10. Correlation between Baicalin Content and SbWRKYs

Nine DEGs (SbWRKY49, SbWRKY16, SbWRKY42, SbWRKY30, SbWRKY43, SbWRKY8, SbWRKY10, SbWRKY14, and SbWRKY37) with a significant relationship with baicalin synthesis were screened from the scutellaria transcriptome, and to understand the expression of SbWRKYs of baicalin, the content was subjected to correlation analysis (Figure 11), from which it can be seen that baicalin content was positively correlated with nine SbWRKYs. SbWRKY8 and SbWRKY49 had correlation coefficients of 1.00 and 0.8, respectively, and negatively correlated with SbWRKY16, with a correlation coefficient of −1.00. Therefore, it can be speculated that the increase in the SbWRKY8 expression level can increase the baicalin content and the decrease in the SbWRKY16 expression level can increase the baicalin content.

3.11. Validation of RNA-seq Data via Quantitative qRT-PCR

To further verify the accuracy of the DEGs profiles of S. baicalensis, the expression levels of nine genes in S. baicalensis screened in Section 3.10 were analyzed via qRT-PCR, including SbWRKY49, SbWRKY16, SbWRKY42, SbWRKY30, SbWRKY43, SbWRKY8, SbWRKY10, SbWRKY14, and SbWRKY37. The results showed that the expression levels of the nine genes determined via qRT-PCR were consistent with the results of sequencing analysis, and the RNA-seq data are reliable (Figure 12).

4. Discussion

The vitally dynamic parts of S. baicalensis are baicalin, wogonoside, and wogonin, and so forth. Previous investigations have discovered that water deficiency might influence the gathering of flavonoids by managing the chemical digestion of Scutellaria baicalin [28,41]. Scutellaria baicalensis Georgi is a mass customary Chinese medication, and its wild assets are seriously taken advantage of, and falsely developed S. baicalensis has a poor quality and an unsatisfactory substance of dynamic fixings, and so on. Transcriptomic exploration can give a hypothetical premise for solutions to these issues. WRKY TFs are one of the biggest groups of recorded factors in the plant realm and are central members in managing the statement of qualities related to embryogenesis, senescence, microorganism opposition, and abiotic stress reactions [42,43,44,45]. WRKY TFs have been identified in various plants, such as Arabidopsis thaliana [46], Zea mays L. [47,48], and Oryza sativa L. [29,49]. There have been a few studies on the role of WRKY TFs during drought stress in S. baicalensis. Sixty WRKYs were identified based on transcriptome data from S. baicalensis using various bioinformatics analysis methods. The SbWRKYs were classified into three major classes (I, II, and III) according to the taxonomy of Arabidopsis, containing 11, 41, and 9 genes, respectively. Class II was further subdivided into four parts (IIa, IIb, IIc, and IIe), with 5, 10, 12, and 13 genes, respectively. This classification is similar to that of WRKY in other plants, indicating similarity among WRKYs [50].

Sixty SbWRKYs were localized to nine chromosomes, with chromosome 3 containing the largest number of SbWRKYs. The conserved element analysis revealed that motif 1, motif 4, and motif 3 are both composed of SbWRKY structural domains, which are present in most genes and that the structural motifs of pairs of genes in the same class are similar, with significant differences and specific motifs between the classes, suggesting that the protein structure functions differ between them. Sixty SbWRKYs all contain WRKY or WRKY superfamily structural domains. Meanwhile, the analysis of the exon and intron structures of SbWRKYs revealed that the number of CDs ranged from one to seven. Notably, SbWRKY57 possesses a WRKYGQK structure and a zinc finger structure, but no intron structure, indicating that chromosomal fragment recombination or deletion may have occurred [51].

The cis-acting element analysis of SbWRKYs and the key enzyme genes revealed that the key enzyme genes contained W box cis-acting elements, suggesting that the key enzyme gene expression may be able to act on SbWRKY TFs to promote or inhibit their expression and that SbWRKYs contain ABRE cis-acting elements, suggesting that the SbWRKY TFs may be involved in the synthesis of ABA hormones. Droughts are accompanied by the production and/or mobilization of the phytohormone, abscisic acid (ABA), which is well known for its ability to induce stomatal closure [52].

Concentrates on WRKYs in plants show that WRKY might assume a significant part in the development and improvement of plants as well as in the reaction to biotic and abiotic stresses. For example, LchiWRKY5, 23, 14, 27, and 36 cross-regulate the response of gooseberry to low-temperature, high-temperature, and drought stresses [36]. It was found that SmWRKY26 and SmWRKY32 have positive effects on the response of eggplant to cold stress [53]. KoWRKY was highly expressed in specific tissues of the obovate autumn eggplant, and nine KoWRKYs were significantly induced in leaves under low-temperature stress [54]. PgWRKY33, PgWRKY62, and PgWRKY65 may be involved in the dehydrating and salt-stress reactions of pearl millet [55].

The outcomes showed that under moderate dry spell pressure, four key compounds were expressed, and moderate and extreme dry season pressure restrained the expression of key chemicals. Baicalin is the most important secondary metabolite of S. baicalensis, and its content is regulated by the key genes encoding enzymes in its biosynthetic pathway [56]. The articulation levels of PAL, C4H, SBCHS-2, and FNSII-2 were fundamentally expanded under gentle dry spell pressure and diminished under moderate and extreme dry season pressure. The articulation levels of each of the four qualities were steady regarding the baicalin content. Among them, PAL and C4H are the two most upstream rate-restricting catalysts in the phenylpropanoid metabolic pathway, controlling the blend of downstream auxiliary metabolites, including flavonoids [57]. Based on the variations in baicalin concentration under various drought stress situations, it is evident that mild drought stress can encourage baicalin accumulation, whereas moderate and severe drought stress will block it.

The interaction between the key enzyme genes and SbWRKYs showed that key enzyme genes acted on SbWRKY15, SbWRKY28, SbWRKY54, SbWRKY33, SbWRKY58, and SbWRKY60, and the analysis of baicalin content with the nine SbWRKYs differential genes showed that the expression of SbWRKY8. The increase in the SbWRKY8 expression level and the decrease in the SbWRKY16 expression level can promote the expression of key enzyme genes, thus promoting the accumulation of baicalin, and the subsequent functional validation of these two genes can determine their relationship with baicalin content.

5. Conclusions

The purpose of this experiment was to examine how baicalin content and SbWRKY TFs interacted under various drought stress scenarios. After analyzing the data, it was determined that the expression of SbWRKYs would change under various drought stress conditions and that there was evidence linking SbWRKYs to the expression of key S. baicalensis enzyme genes and the amount of baicalin in the plant. This relationship suggests that the data could be used to improve the germplasm resources of Scutellaria baicalensis and the quality of Scutellaria baicalensis herbs in the future.