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Article

Transcriptome Analysis of Salvia miltiorrhiza under Drought Stress

by
Siwei Zhang
1,
Xinlan Qi
1,
Ruiyan Zhu
1,2,
Dongdong Ye
1,
Minyu Shou
1,
Lulu Peng
1,
Minghua Qiu
3,
Min Shi
1,* and
Guoyin Kai
1,3,*
1
Laboratory of Medicinal Plant Biotechnology, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China
2
College of Horticulture, Shenyang Agricultural University, Shenyang 110866, China
3
State Key Laboratory of Phytochemistry and Sustainable Utilization of Plant Resources in Western China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China
*
Authors to whom correspondence should be addressed.
Plants 2024, 13(2), 161; https://doi.org/10.3390/plants13020161
Submission received: 14 November 2023 / Revised: 1 January 2024 / Accepted: 4 January 2024 / Published: 6 January 2024
(This article belongs to the Special Issue Synthesis and Regulation of Active Compounds in Medicinal Plants)
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Versions Notes

Abstract

:
Phenolic acids are one of the major secondary metabolites accumulated in Salvia miltiorrhiza with various pharmacological activities. Moderate drought stress can promote the accumulation of phenolic acids in S. miltiorrhiza, while the mechanism remains unclear. Therefore, we performed transcriptome sequencing of S. miltiorrhiza under drought treatment. A total of 47,169 unigenes were successfully annotated in at least one of the six major databases. Key enzyme genes involved in the phenolic acid biosynthetic pathway, including SmPAL, SmC4H, Sm4CL, SmTAT, SmHPPR, SmRAS and SmCYP98A14, were induced. Unigenes annotated as laccase correlated with SmRAS and SmCYP98A14 were analyzed, and seven candidates that may be involved in the key step of SalB biosynthesis by RA were obtained. A total of 15 transcription factors significantly up-regulated at 2 h and 4 h potentially regulating phenolic acid biosynthesis were screened out. TRINITY_DN14213_c0_g1 (AP2/ERF) significantly transactivated the expression of SmC4H and SmRAS, suggesting its role in the regulation of phenolic acid biosynthesis. GO and KEGG enrichment analysis of differential expression genes showed that phenylpropanoid biosynthesis and plant hormone signal transduction were significantly higher. The ABA-dependent pathway is essential for resistance to drought and phenolic acid accumulation. Expression patterns in drought and ABA databases showed that four PYLs respond to both drought and ABA, and three potential SnRK2 family members were annotated and analyzed. The present study presented a comprehensive transcriptome analysis of S. miltiorrhiza affected by drought, which provides a rich source for understanding the molecular mechanism facing abiotic stress in S. miltiorrhiza.

1. Introduction

Salvia miltiorrhiza Bunge (also termed as Danshen) is a traditional Chinese medicine with a rich history for various health benefits, which is commonly referred to as “Red Sage” due to the red color [1,2]. Danshen is native to China and cultivated in other Asian countries as well. The primary bioactive constituents of S. miltiorrhiza include tanshinones and phenolic acids, which contribute to potential positive effects on the cardio-cerebral vascular system [3]. Danshen-based preparations are known for their cardiovascular benefits, providing support for blood vessels, heart function and other health issues. Formulations like Danshen injection and Compound Danshen Dripping Pills are developed by extracting the bioactive component phenolic acids from Danshen roots. Overharvest coupled with unsustainable collection has led to a decrease in the natural Danshen population. Therefore, there is a great necessity to increase the yield to meet the growing clinical needs.
There are several distinct components, such as rosmarinic acid (RA) and salvianolic acid B (SalB), that belong to the phenolic acid class, exhibiting a range of bioactive properties, such as antibacterial, antioxidant and antiviral. The biosynthesis pathway of phenolic acids starts with the conversion of phenylalanine to 4-coumaryl CoA, the process undergoes catalysis of phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H) and 4-coumarate: CoA ligase (4CL) [4]. Another precursor 3,4-hydroxyphenyllactic acid is produced under catalyzation of tyrosine aminotransferase (TAT), 4-hydroxyphenylpyruvate reductase (HPPR) and unknown CYP450 enzyme. Further modifications medicated by rosmarinic acid synthase (RAS) and cytochrome P450-dependent mono-oxygenase (CYP98A14) occur between the two intermediates to produce RA, and then RA is synthesized into SalB through unknown enzymatic reactions. It was speculated that laccase (LAC) may be the key enzyme in the conversion of RA to SalB [5], which still needs to be further studied.
In recent years, various strategies including elicitation, genetic manipulation of rate-limiting enzymes, heterologous gene overexpression, blocking of competitive branches, transcription regulation and post-translational modification have been applied to promote phenolic acid production. For instance, overexpressing single genes, such as PAL, TAT, HPPR, C4H, RAS or CYP98A14, achieved increased phenolic acid production, and co-expressing TAT and HPPR produced the highest amount of RA, which was 4.3-fold higher than that of the wild type [6,7]. Moreover, suppression of HPPD also led to increased phenolic acid content [6]. The introduction of exogenous genes such as AtPAP1 in hairy roots of S. miltiorrhiza has also been investigated to enable enhanced phenolic acid production [8]. Exogenous application of phytohormones triggered the activation of pathway genes to induce the production of phenolic acids. For example, the application of 0.1 mmol/L of methyl jasmonate (MeJA) led to the upregulation of key genes to promote RA and lithospermic acid B (LAB) accumulation [9]. After salicylic acid (SA) treatment for 8 h, phenolic compounds accumulated in the cell suspension and stimulated the activity of PAL [10]. Abscisic acid (ABA) effectively induced the production of phenolic acids by impacting PAL and TAT activity [11]. Meanwhile, multiple transcription factors (TFs) (WRKY, MYB, AP2/ERF, bHLH, bZIP) have been characterized as important players in the regulation of phenolic acid biosynthesis. For example, overexpression of SmPAP1 increased the accumulation of RA and SalB, directly activating SmPAL1 and SmC4H [12]. JA-responsive SmMYB1 is involved in the transcriptional control of SmCYP98A14 to promote phenolic acid biosynthesis [13]. MeJA-induced SmERF115 upregulated phenolic acid content with SmRAS as the target [14]. And bHLH TF, such as SmMYC2, has been shown to significantly promote the accumulation of phenolic acids [15]. ABA-responsive TF SmbZIP1 enhanced the yield of phenolic acids by controlling the biosynthesis gene SmC4H [16]. The SmKFB5 protein controlled the degradation of SmPAL to affect phenolic acid biosynthesis [17].
Among the abiotic stresses that plants faced, drought showed the greatest impact on plant growth, development and metabolism. Plants synthesize ABA under drought conditions, and the ABA-dependent pathway is the core of resistance to drought stress, which may also participate in phenolic acid accumulation. In the absence of ABA, PP2C members dephosphorylate SnRK2 subfamily III members, resulting in the loss of the phosphorylation function of SnRK2 subfamily III members. When plants are exposed to drought stress, ABA biosynthesis is increased, and ABA directly interacts with PP2CA subfamily members after binding to ABA receptors PYL/PYR/RCAR to release TFs and functional genes downstream of phosphorylation of SnRK2 subfamily III members to play a role in drought resistance and secondary metabolism [18]. Drought stress impacted the accumulation of flavonoids in Astragalus membranaceus (Fisch.) Bunge and the content of calycosin-7-O-b-D-glycoside reached the peak after 14 days of drought treatment [19]. Moderate water deficit significantly increased the contents of total phenols, flavonoids and ascorbic acid in leaves of Citrus aurantium L. [20]. Short-term water scarcity increased artemisinin production, but long-term drought stress resulted in decreased levels of artemisinin [21]. With drought treatment, the expression of PAL, C4H, 4CL and CHS in Lotus corniculatus subsp. japonicus (Regel) H. Ohashi was increased, which led to the accumulation of quercetine and kaempferol [22]. Medium drought stress promoted the accumulation of total phenolic acids in S. miltiorrhiza leaves through the regulation of pathway genes [23]. However, the specific mechanism is still unclear.
To investigate the effect of drought stress on S. miltiorrhiza, comprehensive transcriptome sequencing was performed to identify potential genes participating in, or transcription factors regulating, phenolic acid biosynthesis. Moreover, genes associated with drought stress were analyzed, which helps in a deeper exploration of the mechanism of S. miltiorrhiza in response to abiotic stress.

2. Results

2.1. Accumulation of Phenolic Acids in S. miltiorrhiza under Drought Stress

The content of RA and SalB from drought-treated plants under different time points was detected by HPLC (Figure 1). The phenolic acid content reached the maximum after 2 h of drought treatment and then declined. HPLC results revealed that moderate drought will promote phenolic acid accumulation in S. miltiorrhiza.

2.2. Transcriptome Sequencing and De Novo Assembly

In order to further study the regulation mechanism of phenolic acid accumulation, S. miltiorrhiza was induced with natural drought for 0, 2, 4 and 8h for transcriptome sequencing, respectively. Four cDNA libraries containing 52,876,386, 53,994,332, 50,632,334 and 53,097,080 raw reads were constructed (Table 1). The length of the longest and the shortest unigene was 13,359 and 201 bp, respectively, with an average length of 1098.34 bp and an N50 length of 1738 bp. GC content was 43.47%, and BUSCO score was C: 88.2% (Table 2).

2.3. Functional Annotation and Classification of Unigenes

Of the total unigenes, 47,169 were successfully annotated in at least one of the six publicly available protein databases. The annotations were distributed as follows: 17,336 unigenes (36.75%) were annotated in the Gene Ontology (GO) database, 10,992 in the KEGG database, 23,243 in the eggnog database, 20,590 in the NCBI protein non-redundant (NR) database, 20,433 in the Swiss-Prot database and 20,945 in the Pfam database (Table 3). Notably, 8210 unigenes displayed significant matches across all six databases, reflecting comprehensive annotation coverage. In addition, the number of unigenes that were individually annotated into these databases differed, such as 1177 in COG and 0 in GO (Figure 2).

2.4. Expression Patterns of Key Genes and Potential Genes Manipulating Phenolic Acid Biosynthesis in S. miltiorrhiza

The expression patterns of key genes involved in the manipulation of the phenolic acid biosynthesis pathway were investigated. Specifically, unigenes encoding enzymes, such as SmPAL1, SmC4H1, Sm4CL1, SmTAT1, SmHPPR1, SmRAS1 and SmCYP98A14, were analyzed. It was found that the expression levels of these genes were strongly induced and reached to peak at 2h. This expression pattern closely mirrored the accumulation pattern of phenolic acids (Figure 3a). The specific steps from RA to SalB have not been elucidated, and it has been speculated that laccase may play an important role in this process. Therefore, the expression pattern of laccase after drought treatment was analyzed. A total of 50 unigenes were screened and annotated as LACs in the transcriptome (Table S1), and the expression correlation analysis revealed that 7 unigenes showed similar expression patterns with SmRAS1 and SmCYP98A14 (Figure 3b). Namely, TRINITY_DN22797_c0_g1, TRINITY_DN25923_c0_g2 and TRINITY_DN12541_c0_g1 were highly correlated with SmCYP98A14, while TRINITY_DN10883_c0_g2, TRINITY_DN3024_c0_g1, TRINITY_DN9916_c0_g1 and TRINITY_DN7679_c0_g1 were highly correlated with SmRAS1, indicating that these candidate LAC genes may be involved in phenolic acid biosynthesis, which deserved further investigation.

2.5. Analysis of DEGs under Drought Stress

To investigate the impact of drought stress on S. miltiorrhiza at different time points, differential expressed genes (DEGs) were identified. Compared to the control group, a total of 9051 unigenes exhibited differential expression in response to drought stress at 2h, with 3047 up-regulated genes and 6004 down-regulated genes. Similarly, at 4h, 13,286 unigenes showed differential expression, with 4048 up-regulated genes and 9238 down-regulated genes. Drought stress at 8h resulted in the differential expression of 13,013 unigenes, with 4146 up-regulated genes and 8867 down-regulated genes. Notably, the highest number of DEGs (13,286) appeared in the drought-0h-vs.-drought-4h group. However, the largest number of up-regulated genes (4146) was found in the drought-0h versus drought-8h comparison (Figure 4).

2.6. Functional Enrichment Analysis of DEGs

To explore the function of the selected DEGs, the GO database was used to perform significant enrichment analysis (Figure 5a; Figure S1). GO terms enriched by the DEGs of drought 2h and control were analyzed, among which carbohydrate metabolic process enriched in BP (biological process) was the most, and the cellular anatomical entity was enriched in the largest number in CC (cellular component). For MF (molecular function), catalytic activity owned the most number. GO terms involved in DEGs between the drought-4h treatment and the control, which were mainly concentrated in MF, and the cellular anatomical entity (CC, 4647) was the most enriched. GO terms involved in DEGs between 8 h drought treatment and control were mainly enriched in MF, and the most enriched was cellular anatomical entity (CC, 4467).
To explore the biological functions and metabolic pathways of DEGs, KEGG enrichment analysis was applied (Figure 5b). DEGs were involved in 128 metabolic pathways after 2h of drought, 131 metabolic pathways after 4 h of drought and 130 metabolic pathways after 8h of drought, among which the top 10 metabolic pathways were all phenylpropanoid biosynthesis, plant hormone signal transduction, MAPK signaling pathway in plants, plant–pathogen interaction and diterpenoid biosynthesis.

2.7. DEGs of Transcription Factors (TFs) under Drought Stress

Using the PlantTFDB 4.0 database, a total of 1028 TFs were identified and categorized into 20 families. The top 10 TF families were determined based on having more than 35 members, and they included MYB, AP2/ERF, bHLH, C2C2, WRKY, NAC, GRAS, LBD (AS2/LOB), B3 and bZIP (Figure 6a). To investigate the potential TFs involved in regulating the accumulation of phenolic acids under drought stress, 15 differentially expressed TFs were screened. These TFs showed significant up-regulation at 2 and 4h, but no significant difference was observed after 8h (Table 4). Given that phenolic acids, such as RA and SalB, are predominantly found in the roots, the tissue expression profiles of these TFs were analyzed to identify potential candidates involved in phenolic acid biosynthesis (Figure 6b). By examining the tissue-specific expression patterns of the candidate TFs, we aimed to uncover key genes that may play a role in the biosynthesis of phenolic acids. TRINITY_DN7699_c0_g1 (AP2/ERF), TRINITY_DN14213_c0_g1 (AP2/ERF), TRINITY_DN7938_c0_g1 (LBD), TRINITY_DN3906_c0_g2 (C3H) and TRINITY_DN2547_c0_g1 (C2C2) displayed higher accumulation in roots than in other tissues. Meanwhile, the analysis revealed that TRINITY_DN14213_c0_g1 (AP2/ERF) was annotated as DREB in the Swiss-Prot database, and we conducted the Dual-luciferase (Dual-luc) assay (Figure 6c). The LUC/REN value showed that TRINITY_DN14213_c0_g1 (AP2/ERF) significantly transactivated the expression of C4H and RAS, suggesting its role in the regulation of phenolic acid biosynthesis.

2.8. Candidate Genes Involved in ABA Pathway Responding to Drought Stress

Drought stress can induce the increase in ABA, and the increasing ABA can improve the drought tolerance of plants. To investigate the ABA-dependent genes, the expression patterns of the core components of ABA signaling perception and transmission, such as PYL/PYR/RCAR, PP2C and SnRK2, were investigated. According to the comprehensive annotation of six databases, 11 unigenes were annotated as ABA receptors (PYL/PYR/RCAR) in the drought transcriptome, of which 9 unigenes were also annotated as ABA receptors (Table S2). Analysis of expression patterns in drought and ABA databases showed that TRINITY_DN13627_c0_g1, TRINITY_DN5107_c0_g2, TRINITY_DN11812_c0_g2 and TRINITY_DN2396_c0_g1 significantly responded to both drought and ABA (Figure 7a). Nine unigenes were screened to be annotated as SnRK2 family members. After the removal of the three short sequences, the phylogenetic tree of SnRK2 with 10 Arabidopsis thaliana SnRK2s showed that TRINITY_DN5079_c0_g1 (SmSnRK2.3) and TRINITY_DN1822_c0_g1 (SmSnRK2.6) belonged to SnRKIII subfamily, TRINITY_DN27938_c0_g2 and TRINITY_DN3811_c0_g1 belonged to SnRKII subfamily, which were activated by ABA (Figure 7b).

2.9. Expression Analysis of Selected DEGs

In order to certify the reliability of high-throughput transcriptome sequencing and guarantee the correctness of our analysis, eight unigenes that may play an important role in phenolic acid biosynthesis under drought stress were selected for qRT-PCR analysis. Expression pattern of selected unigenes including TRINITY_DN2547_c0_g1 (C2C2), TRINITY_DN7699_c0_g1 (AP2/ERF), TRINITY_DN14213_c0_g1 (AP2/ERF), TRINITY_DN3906_c0_g2 (C3H), TRINITY_DN7938_c0_g1 (LBD), TRINITY_DN2605_c1_g1 (PYL), TRINITY_DN13627_c0_g1 (PYL) and TRINITY_DN33124_c0_g1 (PYL) displayed similar expression trends as RNA-seq, suggesting high data accuracy of the transcriptome sequencing (Figure 8).

3. Discussion

Plants inevitably face a series of biotic and abiotic stresses during their growth. When stress occurs, some complex physiological responses occur in plants to cope with adversity. With the intensification of global warming, drought has gradually become one of the abiotic stresses showing the greatest impact on plant growth and accumulation of distinctive substances. Water limitations have reduced the yield and quality of the crops. Maize (Zea mays L.) and wheat (Triticum aestivum L.) changed their phenotypes (leaf area, the number of grains per year) with drought conditions, resulting in a reduction in yields [24,25]. And drought could affect the accumulation of secondary metabolites in a wide variety of plant species [19,20,22]. As an important medicinal plant, S. miltiorrhiza produces tanshinones and phenolic acids as the main active substances [26,27]. Their production and accumulation are affected by many factors. Exogenous application of ABA, MeJA, yeast extract and Ag+ can stimulate the accumulation of phenolic acids and tanshinones [10,11,28]. In the present study, one-month-old S. miltiorrhiza seedlings were subjected to different drought durations, and total phenolic acids increased first and then decreased; the regulatory mechanism of drought on phenolic acids biosynthesis deserves further exploration.
DEGs under different drought time processing were screened. The number of differentially up-regulated genes in D_2h, D_4h and D_8h groups was 3047, 4048 and 4146, respectively, showing a gradual increase trend, suggesting that with the extension of drought stress, plants have activated a series of complex regulatory mechanisms to up-regulate the expression of related stress protection genes to adapt to or resist drought stress. TFs directly regulate the expression of downstream genes via the interaction with specific cis-elements in the promoter region, making plants adapt to drought stress or enhance plant drought tolerance. For example, SiMYB75 confers sesame (Sesamum indicum L.) drought stress tolerance by promoting stomatal closure to reduce water loss and up-regulating the expression levels of various stress-marker genes in the ABA-dependent pathways [29]. Transcriptome analysis showed that the drought-induced TaMYB31, when ectopically expressed in A. thaliana, increased drought resistance and up-regulated some wax biosynthesis genes and drought response genes in wheat [30]. In order to explore the TFs that regulate phenolic acid biosynthesis under drought stress, we selected 15 candidate TFs based on the results of HPLC and the expression pattern analysis of key enzyme genes in the phenolic acid biosynthesis pathway. Among them, TRINITY_DN14213_c0_g1 was annotated as DREB, which displays homology with the A-1 subfamily of AtDREBs. DREB mainly modulates gene regulation under drought, salt and cold stress in ABA-independent pathways, and most of the A-1 subfamily responded to cold stress [31]. AtDREB1C (AT4G25470.1), AtDREB1B (AT4G25490.1) and AtDREB1A (AT4G25480.1) were reported to respond to cold [32,33,34]. Recent studies displayed that overexpression of AtDREB1A significantly increased tolerance to cold and drought in Arabidopsis and the capacity of drought tolerance in potatoes, and overexpression of AtDREB1B improved the drought resistance in S. miltiorrhiza. Moreover, overexpression of soybean DREB1 increased the drought tolerance of transgenic wheat in the field [35], and overexpression of DREB1A from Oryza sativa L. increased the tolerance of transgenic Arabidopsis to drought, high salt and cold stress [36]. Dual-luc results showed that TRINITY_DN14213_c0_g1 significantly induced the expression of C4H and RAS with LUC/REN value higher than the control. TFs such as SmPAP1 and SmbZIP1 have been confirmed to play a positive role in phenolic acid accumulation by targeting C4H [8,16], while SmERF115 directly modulates the expression of RAS [14], suggesting that TRINITY_DN14213_c0_g1 may play a vital role in phenolic acid biosynthesis.
KEGG enrichment analysis was applied to investigate the metabolic pathways mainly involved in DEGs under different drought times. Phenolic acid components belong to phenylpropanoid biosynthesis, and the significant enrichment of DEGs in this metabolic pathway may lead to increased phenolic acids. The top 10 significant metabolisms in D_2h, D_4h and D_8h groups all included phenylpropanoid biosynthesis, plant hormone signal transduction, MAPK signaling pathway in plants, plant–pathogen interaction, diterpenoid biosynthesis, etc. Under drought stress, signals are transduced through a variety of signaling pathways, involving many second messengers, plant hormones and TFs. Signaling pathways in plant responses to drought stress are mainly ABA-dependent and ABA-independent signal transduction pathways, which convert the initial drought signal into cellular responses, of which the ABA-dependent pathway is the core of drought stress and osmotic stress in plants [18,37]. Three components, PYR/PYL/RCAR, PP2C and SnRK2, are now well known as core components in ABA sensing and signaling [38]. Therefore, we investigated the core components of the ABA-dependent pathway to explore the analytical mechanism of ABA signaling in response to drought stress. A total of 11 unigenes were annotated as PYL/PYR/RCAR, of which 9 could be found in the ABA transcriptome (Table S2). The phylogenetic tree with 14 AtPYLs showed TRINITY_DN13627_c0_g1, TRINI-TY_DN5107_c0_g2 and TRINITY_DN2396_c0_g1 had high homology, and AtPYL7, AtPYL8, AtPYL9 and AtPYL10, which belong to class I, and TRINITY_DN11812_c0_g2 had high homology with AtPYL1 and AtPYR1, which belonged to class III (Figure S2c). The SnRK2 family has a total of 10 members in Arabidopsis and is divided into three subfamilies, of which subfamily I is ABA-independent and mainly regulated by osmotic stress, subfamily III is ABA-dependent as one of the three core components, and subfamily II is less affected by ABA [39]. There are three members of the SnRK2 family in S. miltiorrhiza in NCBI, SmSnRK2.3 and SmSnRK2.6 belong to subfamily III and SmSnRK2.4 belong to subfamily I, which are named TRINITY_DN5079_c0_g1, TRINITY_DN1822_c0_g1 and TRINITY_DN2739_c0_g2 in drought transcriptome, respectively. A total of nine unigenes annotated as SnRK2 were screened. In order to explore the SnRK2 family and their classification, the phylogenetic tree of 9 unigenes and 10 SnRK2s in Arabidopsis was analyzed, and three unigenes were removed due to their short sequences. Based on the phylogenetic tree, SmSnRK2.3 and SmSnRK2.6 were clustered with AtSnRK2.2, AtSnRK2.3 and AtSnRK2.6 in Arabidopsis, which was consistent with the previous study. TRINITY_DN27938_c0_g2 and TRINITY_DN3811_c0_g1 were closely related to AtSnRK2.8 and AtSnRK2.7 of the second subfamily, indicating that these two may be new members of the second subfamily of S. miltiorrhiza SnRK2. TRINITY_DN27938_c0_g1 was closely related to SmSnRK2.4, AtSnRK2.1, AtSnRK2.5, AtSnRK2.4, AtSnRK2.10 and AtSnRK2.9, which might be a new member of SnRK2 subfamily I. In the early stage of drought stress response, before the accumulation of endogenous ABA, the response to drought stress depends on ABA-independent pathway, and the early stress signal will increase ABA synthesis by activating the expression of 9-cis-epoxycartenoid dioxygenase 3 (NCED3) [40]. We analyzed the expression of SmNCED3 under different drought time treatments and found that the expression of SmNCED3 was continuously induced under drought stress, which was significantly up-regulated at 2h of drought stress, reaching 8-fold of the control. TRINITY_DN2739_c0_g2 was annotated as SmSnRK2.4, a SnRK2 class I protein kinase, and its expression was the highest among all annotated members of the SnRK2 family, indicating that the ABA-independent regulatory system may play an important role in response to drought stress as the ABA-dependent system. The transcription factors phosphorylated by SnRK2s in subfamily II and subfamily III of the ABA-dependent regulatory pathway contain AREBs/ABFs, which regulate ABA-responsive genes by recognizing ABA-response element (ABRE) motifs and cause stomata closure, thus improving drought tolerance of plants [41]. Eight unigenes annotated as ABFs were found in the drought transcriptome, among which TRINITY_DN4514_c0_g1 and TRINITY_DN817_c0_g1 were significantly induced under drought stress, suggesting their potential role in response to drought stress in ABA-dependent regulatory pathways.

4. Materials and Methods

4.1. Plant Material and Stress Treatment

Aseptic S. miltiorrhiza plants were grown in the greenhouse of our lab in Zhejiang Chinese Medical University at 25 °C with 16h/8h (light/dark) periods in Murashige and Skoog (MS) basal medium containing 3% sugar and 0.8% agar at the pH of 5.8. One-month-old plants in consistent growth were selected and removed from the culture medium, washed off, dried out and placed in a light incubator (RXZ-500C, Ningbo Jiangnan Instrument, Ningbo, China) at 2000 lx, 25 °C under natural drought treatment. Whole plants were collected after treatment for 0, 2, 4 and 8h, respectively (Figure S3), for HPLC determination of phenolic acids and RNA extraction.

4.2. Analysis of Phenolic Acids

Plant samples were dried at 55 °C and then ground. About 0.1 mg of plant powder was weighed into a 2 mL EP tube and then subjected to ultrasound with 2 mL of 80% methanol for 1 h (KunshanKQ-500B, Kunshan UL transonic instruments, Kunshan, China). The solution was centrifuged with 6500× g for 5 min to obtain supernatant (Eppendorf, Hauppauge, NY, USA) and then filtered with 0.22 μm filter membrane. Agilent 1260 Infinity II was used for the HPLC analysis detector equipped with a reversed-phase C18 symmetry column (Agilent, Santa Clara, CA, USA). Detection parameters of phenolic acid are as follows: mobile phase: water/acetonitrile (7:3, v/v) with pH adjusted to 2.03, flow speed was 1 mL/min and detection wavelength was set to 281 nm. Individual standards were applied to quantify the content of RA and SalB, and the sum of these two compounds amounted to total phenolic acids.

4.3. RNA Isolation and Qualification

Total RNA was extracted from plant samples. Nanodrop2000 (Thermo Fisher Scientific, Waltham, MA, USA) was used to detect the concentration and purity of the extracted RNA; agarose gel electrophoresis was used to detect the RNA integrity; and Agilent5300 (Agilent, USA) was used to determine the RIN value.

4.4. Library Preparation for Transcriptome Sequencing

Transcriptome library was constructed with 1 μg of total RNA with concentration ≥ 30 ng/μL, RIN > 6.5 and OD260/280 between 1.8 and 2.2. Messenger RNA was isolated using the polyA selection method with oligo (dT) beads. Subsequently, the isolated mRNA was fragmented, and then double-stranded cDNA was synthesized with random hexamer primers. Synthesized cDNA underwent end-repair, phosphorylation and the addition of an “A” base. Libraries were size-selected for cDNA fragments of approximately 300 bp using 2% Low Range Ultra Agarose. Subsequently, PCR amplification was performed using Phusion DNA polymerase (NEB) for 15 PCR cycles (T100 Thermal Cycler, BIO-RAD, Hercules, CA, USA). The quantification of the libraries was carried out using the TBS380 system. The NovaSeq 6000 sequencer (Illumina, San Diego, CA, USA) was used to perform sequencing, with a read length of 2 × 150 bp [42].

4.5. Data Filtering and De Novo Assembly

In order to obtain sequencing data in high quality, the raw data of all sequenced samples should be purified and filtered to retain high-quality reads before assembly. The joint sequence was removed from the original sequencing data and also the low-quality sequence (5′ end < 20; 3′ end < 3); reads containing N (fuzzy base), adapters and sequences shorter than 30 bp, during the quality trimming process, underwent a filtration step to retain only high-quality clean reads. These retained reads were then subjected to a de novo assembly using the Trinity program.

4.6. Unique Sequence Functional Annotation and Classification

Diamond (https://github.com/bbuchfink/diamond, accessed on 25 August 2022) and HMMER (ftp://selab.janelia.org/pub/software/hmmer3/3.0/hmmer-3.0.tar.gz, accessed on 25 August 2022) were used to search for assembled transcripts in NR, COG, KEGG, Pfam, Swiss-Prot and GO databases. The alignment was performed using BLASTX, aiming to identify proteins with the highest sequence similarity to the transcripts and retrieve their functional annotations, and a stringent cut-off E-value less than 1.0 × 10−5 was set.

4.7. Quantification of Gene Expression Levels and Differential Expression Analysis

RSEM was used to quantify gene abundances. Essentially, differential expression analysis was performed using DESeq2 (http://bioconductor.org/packages/stats/bioc/DESeq2/, accessed on 25 August 2022) or DEGseq (https://www.rdocumentation.org/packages/DEGseq/versions/1.26.0, accessed on 25 August 2022). Unigenes with |log2FC| ≥ 1 and FDR ≤ 0.05 (DESeq2) or FDR ≤ 0.001 (DEGseq) were considered to be DEGs. Furthermore, functional enrichment analyses were conducted to identify DEGs significantly enriched in specific GO terms and metabolic pathways, which were performed with a significance threshold of p-adjust ≤ 0.05 when compared to the whole transcriptome background.

4.8. Dual-Luc Assay

All the pGREEN0800-promoter (reporter) (promoters of key genes involved in phenolic acid biosynthesis pathway, such as SmPAL1, SmTAT1, Sm4CL1, SmC4H1, SmHPPR1, SmRAS1 and SmCYP98A14) vectors were transformed into Agrobacterium GV3101 with the helper plasmid pSoup-P19. The full-length cDNA sequence of DN14213_c0_g1 was cloned into pHB-YFP to form the effector, together with pHB-YFP as the negative control, and then moved into GV3101. GV3101 harboring the effector or reporter was applied to infiltrate the back leaves of Nicotiana benthamiana. After 24 h dark/24 h light, the infected area was sampled for detection using the Dual-Luciferase Reporter Assay System (Promega, Madison, WI, USA). The LUC/REN ratio indicated the ability of DN14213_c0_g1 to bind to and activate gene promoters.

4.9. Analysis of Gene Expression Level

Samples, drought treated for 0 h, 2 h, 4 h and 8 h, were frozen in liquid nitrogen, and total RNA was extracted using the Tiangen plant total RNA extraction kit. The qRT-PCR experiments were carried out using Thermo Fisher quantitative master mix on the Applied Biosystem Step One Real-Time PCR System (Applied Biosystems, Foster City, CA, USA), with SmActin as the internal control. The relative expression level of selected genes was calculated using the 2–ΔΔCT method. The experiments were conducted three times.

5. Conclusions

Drought affects the accumulation of secondary metabolites in S. miltiorrhiza, but the specific mechanism is still unclear. Comprehensive transcriptome under different drought treatments in S. miltiorrhiza was conducted in our study. Unigene annotated as DREB (TRINITY_DN14213_c0_g1) was screened, which is closely related to the A-1 sub-family and promoted SmC4H and SmRAS expression, suggesting involvement in the biosynthesis of phenolic acids. The ABA pathway plays an important role in plant resistance to drought; four PYL members were significantly responsive to both ABA and drought, and three potential SnRK2 family members were also analyzed. This drought transcriptome will provide new insights into the molecular mechanism of phenolic acid biosynthesis under drought stress.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants13020161/s1, Figure S1. GO functional enrichment analysis of DEGs. (a) GO enrichment analysis of DEGs in D_0h vs. D_4h. (b) GO enrichment analysis of DEGs in D_0h vs. D_8h. The ordinate indicates the GO term, and the abscissa indicates the number of enrichments. The top 10 terms with the largest number were selected for display from MF (molecular function), BP (biological process) and CC (cellular component), respectively; Figure S2. Selected unigenes with gene family in Arabidopsis. The phylogenetic tree was generated using the neighbor-joining method by MEGA11 software. The numerals next to the branch nodes indicate bootstrap values from 1000 replications. (a) The phylogenetic tree includes TF annotated as DREB (TRINITY_DN14213_c0_g1) and the Arabidopsis DREB gene family. The Arabidopsis DREB gene family was downloaded from the Arabidopsis database. (b) Phylogenetic analysis of selected unigene (TRINITY_DN14213_c0_g1) and Arabidopsis DREB A-1 subfamily including AT1G12610.1, AT1G63030.1, AT5G51990.1, AT4G25490.1, AT4G25470.1 and AT4G25480.1. (c) Phylogenetic analysis of 9 PYL/PYR/RCAR both annotated in drought and ABA transcriptome with the Arabidopsis PYL/PYR/RCAR family. AtPYLs were downloaded from the Arabidopsis database. AtPYLs were downloaded from the Arabidopsis database: AtPYL1 (AT5G46790), AtPYL2 (AT2G26040), AtPYL3 (AT1G73000), AtPYL4 (AT2G38310), AtPYL5 (AT5G05440), AtPYL6 (AT2G40330), AtPYL7 (AT4G01026), AtPYL8 (AT5G53160), AtPYL9 (AT1G01360), AtPYL10 (AT4G27920), AtPYL11 (AT5G45860), AtPYL12 (AT5G45870), AtPYL13 (AT4G18620), AtPYR1/RCAR11 (AT4G17870); Figure S3. Physiological state of S. miltiorrhiza plants at 0 h, 2 h, 4 h, 8 h of drought treatment; Table S1. 50 unigenes were annotated as laccase; Table S2. 11 unigenes were annotated as PYL/PYR/RCAR and 9 of them were annotated in both drought and ABA transcriptome databases.

Author Contributions

Conceptualization, M.S. (Min Shi) and G.K.; data curation, S.Z., X.Q., R.Z., M.S. (Minyu Shou), D.Y., L.P. and M.Q.; writing—original draft preparation, S.Z. and M.S. (Min Shi); writing—review and editing, S.Z., M.S. (Min Shi) and G.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by National Natural Science Fund of China (82204554; 82073963), Zhejiang Provincial Natural Science Foundation of China (LQ23H280010), National Young Qihuang Scholars Training Program, the Fund of State Key Laboratory of Phytochemistry and Plant Resources in West China (P2022-KF10), Zhejiang Provincial Program for the Cultivation of High Level Innovative Health Talents, the Science Research Fund of Administration of Traditional Chinese Medicine of Zhejiang Province (2023ZR089).

Data Availability Statement

The data presented in this study are available in the article or Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

List of Abbreviations

RA, rosmarinic acid; SalB, phenolic acid B; PAL, phenylalanine ammonia-lyase; C4H, cinnamic acid-4-hydroxylase; 4CL, 4-coumaric acid-CoA ligase; TAT, tyrosine aminotransferase; HPPR, 4-hydroxyphenylpyruvate reductase; RAS, rosmarinic acid synthase; CYP98A14, cytochrome P450-dependent monooxygenase; LAC, laccase; MeJA, methyl jasmonate; SA, salicylic acid; ABA, abscisic acid; TF, transcription factor; HPLC, high-performance liquid chromatography; TS, total phenolic acids; D_0h, drought treatment for 0 h (before treatment); D_2h, drought treatment for 2 h; D_4h, drought treatment for 4 h; D_8h, drought treatment for 8h; DEG, differential expressed gene; BP, biological process; CC, cellular component; MF, molecular function; Dual-luc, dual-luciferase; NCED3, 9-cis-epoxycartenoid dioxygenase 3; ABRE, ABA-response element.

References

  1. Dong, Y.; Morris-Natschke, S.L.; Lee, K.H. Biosynthesis, total syntheses, and antitumor activity of tanshinones and their analogs as potential therapeutic agents. Nat. Prod. Rep. 2011, 28, 529–542. [Google Scholar] [CrossRef] [PubMed]
  2. Li, Z.M.; Xu, S.W.; Liu, P.Q. Salvia miltiorrhiza Burge (Danshen): A golden herbal medicine in cardiovascular therapeutics. Acta Pharmacol. Sin. 2018, 39, 802–824. [Google Scholar] [CrossRef] [PubMed]
  3. Zhou, L.; Zuo, Z.; Chow, M.S.S. Danshen: An overview of its chemistry, pharmacology, pharmacokinetics, and clinical use. J. Clin. Pharmacol. 2005, 45, 1345–1359. [Google Scholar] [CrossRef] [PubMed]
  4. Hua, W.; Zhang, Y.; Song, J.; Zhao, L.; Wang, Z. De novo transcriptome sequencing in Salvia miltiorrhiza to identify genes involved in the biosynthesis of active ingredients. Genomics 2011, 98, 272–279. [Google Scholar] [CrossRef]
  5. Li, Q.; Feng, J.; Chen, L.; Xu, Z.; Zhu, Y.; Wang, Y.; Xiao, Y.; Chen, J.; Zhou, Y.; Tan, H.; et al. Genome-wide identification and characterization of Salvia miltiorrhiza laccases reveal potential targets for salvianolic acid B biosynthesis. Front. Plant Sci. 2019, 10, 435. [Google Scholar] [CrossRef] [PubMed]
  6. Xiao, Y.; Zhang, L.; Gao, S.; Saechao, S.; Di, P.; Chen, J.; Chen, W. The c4h, tat, hppr and hppd genes prompted engineering of rosmarinic acid biosynthetic pathway in Salvia miltiorrhiza hairy root cultures. PLoS ONE 2011, 6, e29713. [Google Scholar] [CrossRef] [PubMed]
  7. Fu, R.; Shi, M.; Deng, C.; Zhang, Y.; Zhang, X.; Wang, Y.; Kai, G. Improved phenolic acid content and bioactivities of Salvia miltiorrhiza hairy roots by genetic manipulation of RAS and CYP98A14. Food Chem. 2020, 331, 127365. [Google Scholar] [CrossRef]
  8. Zhang, Y.; Yan, Y.P.; Wang, Z.Z. The Arabidopsis PAP1 transcription factor plays an important role in the enrichment of phenolic acids in Salvia miltiorrhiza. J. Agric. Food Chem. 2010, 58, 12168–12175. [Google Scholar] [CrossRef]
  9. Xiao, Y.; Gao, S.; Di, P.; Chen, J.; Chen, W.; Zhang, L. Methyl jasmonate dramatically enhances the accumulation of phenolic acids in Salvia miltiorrhiza hairy root cultures. Physiol Plant. 2009, 137, 1–9. [Google Scholar] [CrossRef]
  10. Dong, J.; Wan, G.; Liang, Z. Accumulation of salicylic acid-induced phenolic compounds and raised activities of secondary metabolic and antioxidative enzymes in Salvia miltiorrhiza cell culture. J. Biotechnol. 2010, 148, 99–104. [Google Scholar] [CrossRef]
  11. Shi, M.; Hua, Q.; Kai, G. Comprehensive transcriptomic analysis in response to abscisic acid in Salvia miltiorrhiza. Plant Cell, Tissue Organ Cult. 2021, 147, 389–404. [Google Scholar] [CrossRef]
  12. Hao, G.; Jiang, X.; Feng, L.; Tao, R.; Li, Y.; Huang, L. Cloning, molecular characterization and functional analysis of a putative R2R3-MYB transcription factor of the phenolic acid biosynthetic pathway in S. miltiorrhiza Bge. f. alba. Plant Cell. Tissue Organ Cult. 2016, 124, 151–168. [Google Scholar] [CrossRef]
  13. Zhou, W.; Shi, M.; Deng, C.; Lu, S.; Huang, F.; Wang, Y.; Kai, G. The methyl jasmonate-responsive transcription factor SmMYB1 promotes phenolic acid biosynthesis in Salvia miltiorrhiza. Hortic. Res. 2021, 8, 10. [Google Scholar] [CrossRef] [PubMed]
  14. Sun, M.; Shi, M.; Wang, Y.; Huang, Q.; Yuan, T.; Wang, Q.; Wang, C.; Zhou, W.; Kai, G. The AP2/ERF transcription factor SmERF115 positively regulates the biosynthesis of phenolic acids in Salvia miltiorrhiza. J Exp Bot. 2019, 70, 243–254. [Google Scholar] [CrossRef] [PubMed]
  15. Deng, H.; Li, Q.; Cao, R.; Ren, Y.; Wang, G.; Guo, H.; Bu, S.; Liu, J.; Ma, P. Overexpression of SmMYC2 enhances salt resistance in Arabidopsis thaliana and Salvia miltiorrhiza hairy roots. J Plant Physiol. 2023, 280, 153862. [Google Scholar] [CrossRef] [PubMed]
  16. Deng, C.; Shi, M.; Fu, R.; Zhang, Y.; Wang, Q.; Zhou, Y.; Wang, Y.; Ma, X.; Kai, G. ABA-responsive transcription factor bZIP1 is involved in modulating biosynthesis of phenolic acids and tanshinones in Salvia miltiorrhiza. J. Exp. Bot. 2020, 71, 5948–5962. [Google Scholar] [CrossRef] [PubMed]
  17. Yu, H.; Li, D.; Yang, D.; Xue, Z.; Li, J.; Xing, B.; Yan, K.; Han, R.; Liang, Z. SmKFB5 protein regulates phenolic acid biosynthesis by controlling the degradation of phenylalanine ammonia-lyase in Salvia miltiorrhiza. J. Exp. Bot. 2021, 72, 4915–4929. [Google Scholar] [CrossRef]
  18. Soma, F.; Takahashi, F.; Suzuki, T.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Plant Raf-like kinases regulate the mRNA population upstream of ABA-unresponsive SnRK2 kinases under drought stress. Nat. Commun. 2020, 11, 1373. [Google Scholar] [CrossRef]
  19. Jia, X.; Sun, C.; Li, G.; Li, G.; Chen, G. Effects of progressive drought stress on the physiology, antioxidative enzymes and secondary metabolites of Radix Astragali. Acta Physiol. Plant. 2015, 37, 262. [Google Scholar] [CrossRef]
  20. Eirini, S.; Paschalina, C.; Ioannis, T.; Kortessa, D.T. Effect of drought and salinity on volatile organic compounds and other secondary metabolites of Citrus aurantium leaves. Nat. Prod. Commun. 2017, 12, 193–196. [Google Scholar] [CrossRef]
  21. Yadav, R.K.; Sangwan, R.S.; Sabir, F.; Srivastava, A.K.; Sangwan, N.S. Effect of prolonged water stress on specialized secondary metabolites, peltate glandular trichomes, and pathway gene expression in Artemisia annua L. Plant Physiol. Biochem. 2014, 74, 70–83. [Google Scholar] [CrossRef] [PubMed]
  22. Garcia-Calderon, M.; Pons-Ferrer, T.; Mrazova, A.; Pal’ove-Balang, P.; Vilkova, M.; Perez-Delgado, C.M.; Vega, J.M.; Eliasova, A.; Repcak, M.; Marquez, A.J.; et al. Modulation of phenolic metabolism under stress conditions in a Lotus japonicus mutant lacking plastidic glutamine synthetase. Front. Plant Sci. 2015, 6, 760. [Google Scholar] [CrossRef]
  23. Liu, H.; Wang, X.; Wang, D.; Zou, Z.; Liang, Z. Effect of drought stress on growth and accumulation of active constituents in Salvia miltiorrhiza Bunge. Ind. Crops Prod. 2011, 33, 84–88. [Google Scholar] [CrossRef]
  24. Naz, S.; Perveen, S. Response of wheat (Triticum aestivum L. Var. Galaxy-2013) to pre-sowing seed treatment with thiourea under drought stress. Pakistan J. Bot. 2021, 53, 1209–1217. [Google Scholar] [CrossRef]
  25. Cai, F.; Zhang, Y.; Mi, N.; Ming, H.; Zhang, S.; Zhang, H.; Zhao, X. Maize (Zea mays L.) physiological responses to drought and rewatering, and the associations with water stress degree. Agric. Water Manag. 2020, 241, 106379. [Google Scholar] [CrossRef]
  26. Xu, H.; Song, J.; Luo, H.; Zhang, Y.; Li, Q.; Zhu, Y.; Xu, J.; Li, Y.; Song, C.; Wang, B.; et al. Analysis of the genome sequence of the medicinal plant Salvia miltiorrhiza. Physiol. Behav. 2017, 176, 139–148. [Google Scholar] [CrossRef]
  27. Song, J.J.; Fang, X.; Li, C.Y.; Jiang, Y.; Li, J.X.; Wu, S.; Guo, J.; Liu, Y.; Fan, H.; Huang, Y.B.; et al. A 2-oxoglutarate-dependent dioxygenase converts dihydrofuran to furan in Salvia diterpenoids. Plant Physiol. 2022, 188, 1496–1506. [Google Scholar] [CrossRef]
  28. Wang, Y.; Shen, Y.; Shen, Z.; Zhao, L.; Ning, D.; Jiang, C.; Zhao, R.; Huang, L. Comparative proteomic analysis of the response to silver ions and yeast extract in Salvia miltiorrhiza hairy root cultures. Plant Physiol. Biochem. 2016, 107, 364–373. [Google Scholar] [CrossRef]
  29. Dossa, K.; Mmadi, M.A.; Zhou, R.; Liu, A.; Yang, Y.; Diouf, D.; You, J.; Zhang, X. Ectopic expression of the sesame MYB transcription factor SiMYB305 promotes root growth and modulates ABA-mediated tolerance to drought and salt stresses in Arabidopsis. AoB Plants 2019, 12, plz081. [Google Scholar] [CrossRef]
  30. Zhao, Y.; Cheng, X.; Liu, X.; Wu, H.; Bi, H.; Xu, H. The wheat MYB transcription factor TaMYB31 is involved in drought stress responses in Arabidopsis. Front. Plant Sci. 2018, 9, 1426. [Google Scholar] [CrossRef]
  31. Lata, C.; Prasad, M. Role of DREBs in regulation of abiotic stress responses in plants. J. Exp. Bot. 2011, 62, 4731–4748. [Google Scholar] [CrossRef]
  32. Liu, Q.; Kasuga, M.; Sakuma, Y.; Abe, H.; Miura, S. Two transcription factors, DREB1 and DREB2, with an EREBP/AP2 DNA binding domain separate two cellular signal transduction pathways in drought- and low-temperature-responsive gene expression, respectively, in Arabidopsis. Plant Cell Online 1998, 10, 1391–1406. [Google Scholar] [CrossRef] [PubMed]
  33. Jia, X.X.; Li, Y.T.; Qi, E.F.; Ma, S.; Hu, X.Y.; Wen, G.H.; Wang, Y.H.; Li, J.W.; Zhang, X.H.; Wang, H.M.; et al. Overexpression of the Arabidopsis DREB1A gene enhances potato drought-resistance. Russ. J. Plant Physiol. 2016, 63, 523–531. [Google Scholar] [CrossRef]
  34. Wei, T.; Deng, K.; Gao, Y.; Liu, Y.; Yang, M.; Zhang, L.; Zheng, X.; Wang, C.; Song, W.; Chen, C.; et al. Arabidopsis DREB1B in transgenic Salvia miltiorrhiza increased tolerance to drought stress without stunting growth. Plant Physiol. Biochem. 2016, 104, 17–28. [Google Scholar] [CrossRef] [PubMed]
  35. Zhou, Y.; Chen, M.; Guo, J.; Wang, Y.; Min, D.; Jiang, Q.; Ji, H.; Huang, C.; Wei, W.; Xu, H.; et al. Overexpression of soybean DREB1 enhances drought stress tolerance of transgenic wheat in the field. J. Exp. Bot. 2020, 71, 1842–1857. [Google Scholar] [CrossRef]
  36. Dubouzet, J.G.; Sakuma, Y.; Ito, Y.; Kasuga, M.; Dubouzet, E.G.; Miura1, S.; Seki, M.; Shinozaki, K.; Kazuko, Y.S. OsDREB genes in rice, Oryza sativa L., encode transcription activators that function in drought-, high-salt- and cold-responsive gene expression. Plant J. 2011, 79, 100–102. [Google Scholar] [CrossRef] [PubMed]
  37. Soma, F.; Takahashi, F.; Yamaguchi-Shinozaki, K.; Shinozaki, K. Cellular Phosphorylation signaling and gene expression in drought stress responses: ABA-dependent and ABA-independent regulatory systems. Plants 2021, 10, 756. [Google Scholar] [CrossRef] [PubMed]
  38. Dittrich, M.; Mueller, H.M.; Bauer, H.; Peirats-Llobet, M.; Rodriguez, P.L.; Geilfus, C.M.; Carpentier, S.C.; Al Rasheid, K.A.S.; Kollist, H.; Merilo, E.; et al. The role of Arabidopsis ABA receptors from the PYR/PYL/RCAR family in stomatal acclimation and closure signal integration. Nat. Plants 2019, 5, 1002–1011. [Google Scholar] [CrossRef]
  39. Umezawa, T.; Nakashima, K.; Miyakawa, T.; Kuromori, T.; Tanokura, M.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Molecular basis of the core regulatory network in ABA responses: Sensing, signaling and transport. Plant Cell Physiol. 2010, 51, 1821–1839. [Google Scholar] [CrossRef]
  40. Kalladan, R.; Lasky, J.R.; Sharma, S.; Kumar, M.N.; Juenger, T.E.; Des Marais, D.L.; Verslues, P.E. Natural variation in 9-cis-epoxycartenoid dioxygenase 3 and ABA accumulation. Plant Physiol. 2019, 179, 1620–1631. [Google Scholar] [CrossRef]
  41. Yoshida, T.; Fujita, Y.; Maruyama, K.; Mogami, J.; Todaka, D.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Four Arabidopsis AREB/ABF transcription factors function predominantly in gene expression downstream of SnRK2 kinases in abscisic acid signalling in response to osmotic stress. Plant Cell Environ. 2015, 38, 35–49. [Google Scholar] [CrossRef]
  42. Ren, Y.; Yu, G.; Shi, C.; Liu, L.; Guo, Q.; Han, C.; Zhang, D.; Zhang, L.; Liu, B.; Gao, H.; et al. Majorbio Cloud: A one-stop, comprehensive bioinformatic platform for multiomics analyses. iMeta 2022, 1, e12. [Google Scholar] [CrossRef]
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Figure 1. Phenolic acid content in drought-treated S. miltiorrhiza plants at 0, 2, 4, 8h. D_0h, drought treatment for 0h (before treatment); D_2h, drought treatment for 2h; D_4h, drought treatment for 4h; D_8h, drought treatment for 8h. RA, rosmarinic acid; Sal B, salvianolic acid B; TS, total phenolic acids. Error bars indicate SD (n = 3), * p < 0.05.
Figure 1. Phenolic acid content in drought-treated S. miltiorrhiza plants at 0, 2, 4, 8h. D_0h, drought treatment for 0h (before treatment); D_2h, drought treatment for 2h; D_4h, drought treatment for 4h; D_8h, drought treatment for 8h. RA, rosmarinic acid; Sal B, salvianolic acid B; TS, total phenolic acids. Error bars indicate SD (n = 3), * p < 0.05.
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Figure 2. Annotation of unigenes in six public protein databases (NR, Pfam, Swiss-Prot, GO, eggnog and KEGG). The bar chart on the left represents the total number of unigenes annotated in the corresponding database, the lighted circle on the lower right indicates that unigenes have been annotated to the corresponding database; the connecting line indicates unigenes annotated simultaneously, and the number above indicates the number of unigenes annotated in the databases corresponding to the connected lighted circle.
Figure 2. Annotation of unigenes in six public protein databases (NR, Pfam, Swiss-Prot, GO, eggnog and KEGG). The bar chart on the left represents the total number of unigenes annotated in the corresponding database, the lighted circle on the lower right indicates that unigenes have been annotated to the corresponding database; the connecting line indicates unigenes annotated simultaneously, and the number above indicates the number of unigenes annotated in the databases corresponding to the connected lighted circle.
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Figure 3. Expression profile of selected unigenes. (a) Expression of key enzyme genes in the biosynthetic pathway of phenolic acids. The expression levels of biosynthetic genes at different times of drought treatment were presented in a heatmap using line normalization criteria. FPKM values of unigenes were transformed by log2. The redder the color, the higher the expression level, and the bluer the color, the lower the expression level. (b) Correlation expression profile of selected laccases with SmRAS1 and SmCYP98A14. After FPKM normalization, 7 of the 50 unigenes annotated as LAC were found to have similar expression trends to SmRAS1 and SmCYP98A14.
Figure 3. Expression profile of selected unigenes. (a) Expression of key enzyme genes in the biosynthetic pathway of phenolic acids. The expression levels of biosynthetic genes at different times of drought treatment were presented in a heatmap using line normalization criteria. FPKM values of unigenes were transformed by log2. The redder the color, the higher the expression level, and the bluer the color, the lower the expression level. (b) Correlation expression profile of selected laccases with SmRAS1 and SmCYP98A14. After FPKM normalization, 7 of the 50 unigenes annotated as LAC were found to have similar expression trends to SmRAS1 and SmCYP98A14.
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Figure 4. DEGs in different drought conditions. The gene expression levels of genes under drought treatment at 2 h (D_2h), 4 h (D_4h) and 8 h (D_8h) compared with the control (D_0h), respectively. “Up” meant that expression levels were significantly up-regulated compared with the control, and “down” represented down-regulation after drought treatment when compared with the control.
Figure 4. DEGs in different drought conditions. The gene expression levels of genes under drought treatment at 2 h (D_2h), 4 h (D_4h) and 8 h (D_8h) compared with the control (D_0h), respectively. “Up” meant that expression levels were significantly up-regulated compared with the control, and “down” represented down-regulation after drought treatment when compared with the control.
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Figure 5. Functional enrichment analysis of DGEs. (a) GO enrichment analysis of DEGs in D_0h vs. D_2h. The ordinate indicates the GO term, and the abscissa indicates the number of enrichments. The top 10 terms with the largest number from MF (molecular function), BP (biological process) and CC (cellular component), respectively, were selected for display. (b) KEGG enrichment analysis of DEGs under drought treatment. Red, D_0h vs. D_2h; blue, D_0h vs. D_4h; green, D_0h vs. D_8h.
Figure 5. Functional enrichment analysis of DGEs. (a) GO enrichment analysis of DEGs in D_0h vs. D_2h. The ordinate indicates the GO term, and the abscissa indicates the number of enrichments. The top 10 terms with the largest number from MF (molecular function), BP (biological process) and CC (cellular component), respectively, were selected for display. (b) KEGG enrichment analysis of DEGs under drought treatment. Red, D_0h vs. D_2h; blue, D_0h vs. D_4h; green, D_0h vs. D_8h.
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Figure 6. Analysis of DEGs annotated as TFs. (a) The top 10 TF families with more than 35 members were listed. (b) Expression profile of 15 TFs in different drought treatments and different tissues in S. miltiorrhiza. D_0h, D_2h, D_4h and D_8h represented drought treatment at 0h, 2h, 4h and 8h, respectively. F, flower; S, stem; L, leaf; R, root. FPKM values of unigenes were transformed by log2. The redder the color, the higher the expression level, and the bluer the color, the lower the expression level. (c) Dual-luc analysis showing that DN14213_c0_g1 activated SmC4H and SmRAS. The values were calculated by the LUC/REN ratio. Error bars show ± SD (n = 3), * p < 0.05, ** p < 0.01.
Figure 6. Analysis of DEGs annotated as TFs. (a) The top 10 TF families with more than 35 members were listed. (b) Expression profile of 15 TFs in different drought treatments and different tissues in S. miltiorrhiza. D_0h, D_2h, D_4h and D_8h represented drought treatment at 0h, 2h, 4h and 8h, respectively. F, flower; S, stem; L, leaf; R, root. FPKM values of unigenes were transformed by log2. The redder the color, the higher the expression level, and the bluer the color, the lower the expression level. (c) Dual-luc analysis showing that DN14213_c0_g1 activated SmC4H and SmRAS. The values were calculated by the LUC/REN ratio. Error bars show ± SD (n = 3), * p < 0.05, ** p < 0.01.
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Figure 7. Analysis of ABA-dependent signaling pathway genes under drought stress. (a) Correlation analysis of 9 PYLs in drought and ABA transcriptome. D_0h, D_2h, D_4h and D_8h represented drought treatment at 0, 2, 4 and 8h, respectively. ABA1_0h, ABA1_2h, ABA1_4h and ABA1_8h represented ABA treatment of 50 μmol/L at 0, 2, 4 and 8h, respectively. FPKM values of unigenes were transformed by log2, and row cluster analysis was used. (b) Phylogenetic tree of 6 unigenes annotated SmSnRK2s with SnRK2 members of A. thaliana. The phylogenetic tree was generated using the neighbor-joining method by MEGA11 software. The numerals next to the branch nodes indicate bootstrap values from 1000 replications. AtSnRKs were downloaded from the Arabidopsis database: AtSnRK2.1 (AT5G08590), AtSnRK2.2 (AT3G50500), AtSnRK2.3 (AT5G66880), AtSnRK2.4 (AT1G10940), AtSnRK2.5 (AT5G63650), AtSnRK2.6 (AT4G33950), AtSnRK2.7 (AT4G40010), AtSnRK2.8 (AT1G78290), AtSnRK2.9 (AT2G23030) and AtSnRK2.10 (AT1G60940).
Figure 7. Analysis of ABA-dependent signaling pathway genes under drought stress. (a) Correlation analysis of 9 PYLs in drought and ABA transcriptome. D_0h, D_2h, D_4h and D_8h represented drought treatment at 0, 2, 4 and 8h, respectively. ABA1_0h, ABA1_2h, ABA1_4h and ABA1_8h represented ABA treatment of 50 μmol/L at 0, 2, 4 and 8h, respectively. FPKM values of unigenes were transformed by log2, and row cluster analysis was used. (b) Phylogenetic tree of 6 unigenes annotated SmSnRK2s with SnRK2 members of A. thaliana. The phylogenetic tree was generated using the neighbor-joining method by MEGA11 software. The numerals next to the branch nodes indicate bootstrap values from 1000 replications. AtSnRKs were downloaded from the Arabidopsis database: AtSnRK2.1 (AT5G08590), AtSnRK2.2 (AT3G50500), AtSnRK2.3 (AT5G66880), AtSnRK2.4 (AT1G10940), AtSnRK2.5 (AT5G63650), AtSnRK2.6 (AT4G33950), AtSnRK2.7 (AT4G40010), AtSnRK2.8 (AT1G78290), AtSnRK2.9 (AT2G23030) and AtSnRK2.10 (AT1G60940).
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Figure 8. qRT-PCR and RNA-seq data of selected unigenes. (a–h) qRT-PCR and RNA-seq data of the unigenes which played important role in the biosynthesis of phenolic acids or regulation of drought stress mentioned in this study. The qRT-PCR data are shown in a blue bar chart, with a relative expression on the left Y-axis; error bars represent ± SD of triplicates for qRT-PCR, and an asterisk denotes statistically significant differences with D_0h. D_0h, drought for 0h (control); D_2h, drought for 2h; D_4h, drought for 4h; D_8h, drought for 8h. * p < 0.05; ** p < 0.01; *** p < 0.001. The corresponding data for selected unigenes in transcriptome data shown as RNA-seq are in the orange bar plots chart with FPKM values on the right Y-axis, and FPKM values of unigenes were transformed by log2.
Figure 8. qRT-PCR and RNA-seq data of selected unigenes. (a–h) qRT-PCR and RNA-seq data of the unigenes which played important role in the biosynthesis of phenolic acids or regulation of drought stress mentioned in this study. The qRT-PCR data are shown in a blue bar chart, with a relative expression on the left Y-axis; error bars represent ± SD of triplicates for qRT-PCR, and an asterisk denotes statistically significant differences with D_0h. D_0h, drought for 0h (control); D_2h, drought for 2h; D_4h, drought for 4h; D_8h, drought for 8h. * p < 0.05; ** p < 0.01; *** p < 0.001. The corresponding data for selected unigenes in transcriptome data shown as RNA-seq are in the orange bar plots chart with FPKM values on the right Y-axis, and FPKM values of unigenes were transformed by log2.
Plants 13 00161 g008
Table 1. Summary of transcripts induced by different times after drought stress in S. miltiorrhiza.
Table 1. Summary of transcripts induced by different times after drought stress in S. miltiorrhiza.
SampleRaw ReadsRaw BasesClean ReadsClean BasesError Rate (%)Q20 (%)Q30 (%)GC Content (%)
D_0h52,876,3867,984,334,28652,499,0067,692,810,2760.023598.5995.6649.75
D_2h53,994,3328,153,144,13253,596,3507,812,370,0160.023398.6895.9549.92
D_4h50,632,3347,645,482,43450,209,7827,314,924,3920.023398.6795.9449.33
D_8h53,097,0808,017,659,08052,658,7807,610,216,2780.023398.6895.9849.4
Table 2. Summary of sequence assembly results.
Table 2. Summary of sequence assembly results.
TypeResource
Total number95,836
Total base105,260,765
Largest length (bp)13,359
Smallest length (bp)201
Average length (bp)1098.34
N50 length (bp)1738
E90N50 length (bp)2071
Mean mapped percent (%)86.204
GC percent (%)43.47
TransRate score0.34775
BUSCO scoreC: 88.2% (S: 54.0%, D: 34.2%), F: 4.5%, M: 7.3%, n: 1440
Table 3. Functional annotation of unigenes in different databases.
Table 3. Functional annotation of unigenes in different databases.
DatabaseExp_Unigene Number (Percent)
GO17,336 (0.3675)
KEGG10,992 (0.233)
eggNOG23,243 (0.4928)
NR20,590 (0.4365)
Swiss-Prot20,433 (0.4332)
Pfam20,945 (0.444)
Total_anno25,936 (0.5499)
Total47,169 (1)
Table 4. Fifteen TFs with screening criteria were selected.
Table 4. Fifteen TFs with screening criteria were selected.
Gene_IDFamilyD_0h vs. D_2h D_0h vs. D_4hD_0h vs. D_8h
TRINITY_DN2547_c0_g1C2C2upupno
TRINITY_DN21224_c0_g3MYBupupno
TRINITY_DN7938_c0_g1LBD (AS2/LOB)upupno
TRINITY_DN7699_c0_g1AP2/ERFupupno
TRINITY_DN14213_c0_g1AP2/ERFupupno
TRINITY_DN16066_c0_g1AP2/ERFupupno
TRINITY_DN9724_c1_g2AP2/ERFupupno
TRINITY_DN13435_c0_g1WRKYupupno
TRINITY_DN12650_c0_g1WRKYupupno
TRINITY_DN5238_c0_g2B3upupno
TRINITY_DN3906_c0_g2C3Hupupno
TRINITY_DN22515_c0_g1CAMTAupupno
TRINITY_DN25668_c0_g1bHLHupupno
TRINITY_DN3400_c0_g1bHLHupupno
TRINITY_DN3083_c1_g2CPPupupno
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MDPI and ACS Style

Zhang, S.; Qi, X.; Zhu, R.; Ye, D.; Shou, M.; Peng, L.; Qiu, M.; Shi, M.; Kai, G. Transcriptome Analysis of Salvia miltiorrhiza under Drought Stress. Plants 2024, 13, 161. https://doi.org/10.3390/plants13020161

AMA Style

Zhang S, Qi X, Zhu R, Ye D, Shou M, Peng L, Qiu M, Shi M, Kai G. Transcriptome Analysis of Salvia miltiorrhiza under Drought Stress. Plants. 2024; 13(2):161. https://doi.org/10.3390/plants13020161

Chicago/Turabian Style

Zhang, Siwei, Xinlan Qi, Ruiyan Zhu, Dongdong Ye, Minyu Shou, Lulu Peng, Minghua Qiu, Min Shi, and Guoyin Kai. 2024. "Transcriptome Analysis of Salvia miltiorrhiza under Drought Stress" Plants 13, no. 2: 161. https://doi.org/10.3390/plants13020161

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