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Article

Identification of the Genome-Wide Expression Patterns of Non-Coding RNAs Associated with Tanshinones Synthesis Pathway in Salvia miltiorrhiza

by
Caicai Lin
1,2,†,
Changhao Zhou
1,†,
Zhongqian Liu
1,
Xingfeng Li
1,3 and
Zhenqiao Song
1,*
1
Agronomy College, Shandong Agricultural University, Tai’an 271018, China
2
Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China
3
State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(2), 321; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13020321
Submission received: 25 October 2022 / Revised: 22 December 2022 / Accepted: 29 December 2022 / Published: 20 January 2023
(This article belongs to the Special Issue Research Progress and Application Prospect of Medicinal Plants)
Browse Figures
Versions Notes

Abstract

:
The red root of Salvia miltiorrhiza Bunge, a famous traditional Chinese medicine (TCM), was caused by tanshinone in epidermis cells. In order to study the biological function of ncRNAs in the tanshinone synthesis, the expression patterns of mRNA and ncRNAs were comprehensively analyzed in red (high tanshinone content) and white root (low tanshinone content) tissues derived from the same plant. A total of 731 differentially expressed genes (DEGs) were mainly enriched in primary metabolic pathways such as galactose and nitrogen, and some secondary metabolic pathways such as phenylpropanoid and terpenoids. A total of 70 miRNAs, 48 lncRNAs, and 26 circRNAs were identified as differentially expressed (DE) ones. The enrichment pathway of the targets of DE-lncRNA were mainly in ribosome, carbon metabolism, plant hormone signal transduction, and glycerophospholipid metabolism. The function of the targets genes of 59 miRNAs combined with DE-circRNAs was mainly involved in plant–pathogen interaction, endocytosis, phenylpropanoid biosynthesis, and sesquiterpenoid and triterpenoid biosynthesis pathways. Most genes of the tanshinone synthesis pathway had a higher expression. Some ncRNAs were predicted to regulate several key enzyme genes of the tanshinone synthesis pathway, such as SmDXS2, SmGGPPS1, SmKSL. Furthermore, most target genes were related to the resistance of pathogens. The present study exhibited the tissue-specific expression patterns of ncRNAs, which would provide a basis for further research into the regulation mechanism of ncRNAs in the tanshinone synthesis process.

1. Introduction

Salvia miltiorrhiza Bunge (S. miltiorrhiza) is a traditional Chinese medicine (TCM) in the genus Salvia (sage), one of the largest and most important aromatic and medicinal genera in Lamiaceae. Its red roots, known as “Danshen” or “red sage” due to its characteristic red pigment, have been used in TCM for over 2000 years [1]. It has been widely applied in treating cardio-cerebrovascular diseases, such as angina, coronary heart disease, and myocardial infarction in China and other Asian countries for thousands of years [2].
The main active components of red root in S. miltiorrhiza are lipid-soluble tanshinones and water-soluble phenolic acids compounds [3,4]. Tanshinones are a special kind of diterpenoid quinone and additionally pigment components in S. miltiorrhiza. About 40 kinds of tanshinones have been isolated and identified, such as tanshinone IIA (flaky orange crystal), tanshinone I (reddish-brown columnar crystal), tanshinone IIB (purple acicular crystal) [5,6,7]. Tanshinones are mainly biosynthesized and accumulated in the epidermis of the S. miltiorrhiza root. Several essential genes were involved in the pathway of tanshinone biosynthesis, which were mainly expressed in the epidermis tissue [8,9,10]. So far, most of the key enzyme genes have been found and characterized. Recent studies confirmed that several essential genes (SmCYP76AH1, SmCYP76AH3, SmCYP76AK1, SmCYP71D373, SmCYP71D375, Sm2-ODD14) were involved in the downstream pathway of tanshinone synthesis. [11,12,13,14]. These results provide a favorable basis for further exploring some regulatory factors affecting tanshinone synthesis and metabolism.
Whole-transcriptome sequencing analysis can integrate various RNA informations, including mRNA and ncRNA (miRNA, lncRNA, and circRNA), and comprehensively explore the potential network of the regulation mechanism at the RNA level [15,16,17]. Recent progress has revealed that miRNAs have an essential regulatory function in the secondary metabolism of the plant [18,19,20,21,22,23]. Different reports have also proved that lncRNAs play a significant role in a wide range of cellular mechanisms, from almost all aspects of gene expression to protein translation and stability [24,25,26,27,28]. The regulation and function mechanism of lncRNAs was mainly identified in the model plant, such as Zea mays L., Oryza sativa L., Arabidopsis thaliana (L.) Heynh., and mainly focuses on growth and development, and stress response as well as reproductive development [29,30,31,32]. The spatiotemporal expression characters of circRNAs suggested that they may be involved in gene regulation and in affecting the development of different tissues [33,34,35] and that they participated in regulatory abiotic and biological stress in plants [36,37]. CircRNA can also regulate the transcriptional expression of source genes and act as a competitive endogenous RNA (ceRNA) to bind intracellular miRNAs and block the inhibition of miRNAs on target genes. [38]. Until now, the current research on the function of ncRNAs in S. miltiorrhiza has not been sufficient.
In order to interpret the function of ncRNAs in the development and accumulation of active components in S. miltiorrhiza, red and white roots were used as materials to conduct noncoding RNA sequencing analysis in the present study. The results would benefit from predicting and screening potential ncRNAs involved in the synthesis of active components and exploring their function in the growth and development of S.miltiorrhiza.

2. Materials and Methods

2.1. Plant Materials and Growth Conditions

The S. miltiorrhiza line DSS3 used in this experiment were grown in a glass bottle with a diameter of 10 cm and height of 40 cm in the State Key Laboratory of Crop Biology, Shandong Agricultural University. Plantlets were harvested after being grown for 6 months, washed with distilled water, divided into phloem of the white root and phloem of the red root, both containing periderm. In the designed experiment, these red root and white root tissues derived from the same plant with a similar structure reflected the least different genetic background. The tissues were immediately frozen in liquid nitrogen and stored at −80 °C for further experiments.

2.2. HPLC Analysis of Tanshinone Content

Analysis of the tanshinone content differences of the two root samples was carried out using high-performance liquid chromatography (HPLC). The methods of extraction and detection used in this study were established by our laboratory [39] and mainly used for quantitation of known components (Supplementary Figure S1). Chromatographic separations were carried out in a reverse-phase C18 column (250 × 4.6 mm, five μm particle size; Thermo, Waltham, MA, USA), and a 20.0 × 4.6 mm guard column connected to a Waters 600E HPLC System which was equipped with an auto-injector, UV detector, and Empowers software (Waters Associates, Milford, MA, USA).

2.3. Methods of RNA Extraction, Detection, and Profound Sequence of ncRNAs

The RNA samples were extracted with Trizol. The integrity of the RNA was assessed using the RNA Nano 6000 Assay Kit (Agilent Technologies, Palo Alto, CA, USA). The concentration and purity of the RNA were measured using the NanoDrop 2000 Spectrophotometer (Thermo, Waltham, MA, USA).

2.4. Library Preparation for sRNA Sequencing, lncRNA-Seq and circRNA-Seq

A total of 2.5 ng RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using an NEB Next Ultra-small RNA Sample Library Prep Kit for Illumina (NEB) following the manufacturer’s recommendations. A total of 1.5 μg RNA per sample was used as input material for rRNA removal using the Ribo-Zero rRNA Removal Kit (Epicentre, Madison, WI, USA). Sequencing libraries were generated using an NEBNextR UltraTM Directional RNA Library Prep Kit for Illumina (NEB). The manufacturer’s recommendations and index codes were added to attribute sequences to each sample for lncRNA-Seq and circRNA-Seq.

2.5. Clustering, Sequencing, and Quality Control

According to the manufacturer’s instructions, the clustering of the index-coded samples was performed on a cBot Cluster Generation System using a TruSeq PE Cluster Kit v4-cBot-HS (Illumina, San Diego, CA, USA). After cluster generation, the library preparations were sequenced on an Illumina Hiseq Xten platform, and paired-end reads were generated. Raw data (raw reads) from the fastq format were first processed through in-house Perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing an adapter, reads containing a ploy-N, and low-quality reads from raw data. The miRNA reads were trimmed and cleaned by removing the sequences smaller than 18 nt or longer than 30 nt. At the same time, the Q20, Q30, GC-content, and sequence duplication levels of the clean data were calculated.

2.6. Computational Identification of ncRNAs

By Bowtie (v1.0.0, http://bowtie-bio.sourceforge.net/index.shtml, accessed on 10 January 2018), the clean reads were performed via a sequence alignment, respectively with a Silva database (http://www.arb-silva.de/, accessed on 22 January 2018), GtRNAdb database (http://lowelab.ucsc.edu/GtRNAdb/, accessed on 27 January 2018), Rfam database (http://rfam.xfam.org/, accessed on 3 February 2018) and Repbase database (http://www.girinst.org/repbase/, accessed on 9 February 2018); then filter ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA) and other ncRNA and repeats. Finally, unannotated reads containing miRNA were obtained and mapped with the reference genome of S. miltiarrhiza.
The mapped reads were further used to detect known miRNA by comparing them with known miRNAs from the miRBase (http://www.mirbase.org/, accessed on 20 February 2018) and potential miRNAs by miRDeep2 with suitable parameters. Possible precursor sequences were obtained via position information of reads on the genome. Randfold tools software was used for potential miRNA secondary structure prediction based on the Bayesian model algorithm.
The transcriptome was assembled using the StringTie (https://0-ccb-jhu-edu.brum.beds.ac.uk/software/stringtie/index.shtml, accessed on 24 February 2018). The assembled transcripts were then annotated using the gffcompare program. The unknown transcripts were used to screen for putative lncRNAs. Three computational approaches including CPC (http://cpc.cbi.pku.edu.cn/, accessed on 4 March 2018), CNCI (http://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/pubmed/23892401, accessed on 9 March 2018), Pfam (http://pfam.xfam.org/, accessed on 15 March 2018), and CPAT (http://lilab.research.bcm.edu/cpat/, accessed on 19 March 2018) were combined to sort non-protein coding RNA candidates from putative protein-coding RNAs in the unknown transcripts. Putative protein-coding RNAs were filtered out using a minimum length and exon number threshold. Transcripts of more than 200 nt in length and more than two exons were selected as lncRNA candidates and further screened using CPC/CNCI/Pfam/CPAT. CircRNAs were identified by find_circ software.

2.7. Differential Expression Analysis

Differential expression analysis was performed using the DESeq R package (1.10.1, http://www.bioconductor.org/packages/release/bioc/html/DESeq.html, accessed on 28 March 2018). MiRNA/genes with an adjusted p < 0.01 found by DESeq were assigned as differentially expressed. The Q value < 0.01 & |log2 (Fold Change) | ≥ 1 was set as the threshold for significantly differential expression.
For lncRNAs and circRNAs, the edgeR program package adjusted the read counts through one scaling normalized factor. Differential expression analysis was performed using the EBseq (2010, https://www.biostat.wisc.edu/~kendzior/EBSEQ/, accessed on 13 April 2018) R package. The FDR (false discovery rate) < 0.05 & |log2(Fold Change)| ≥1 was set as the threshold for significantly differential expression. The p-value was used as the key index for screening differentially expressed circRNAs.

2.8. Validation by Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)

Total RNA was obtained and purified from the phloem and epidermis of red and white roots using an RNAprep Pure Plant Plus Kit (Polysaccharides&Polyphenolics-rich) (Tiangen Biotech, Beijing, China). No less than 1 μg total RNA was used to synthesize cDNA by HiScript III 1st Strand cDNA Synthesis Kit (+gDNA wiper) (Vazyme, Nanjing, China) according to the manufacturer’s instructions. The miRNAs were isolated using an miRcute Plant miRNA Isolation Kit (Tiangen Biotech). The highly efficient poly(A) tail addition and first-strand cDNA synthesis were performed using a TransScript® miRNA RT Enzyme Mix and 2×TS miRNA Reaction Mix (Transgen Biotech). QRT-PCR was performed using a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems, Waltham, MA, USA) and TransStart® Top Green qPCR SuperMix (Transgen Biotech) with gene-specific primer pairs. The reaction procedure of the qPCR was performed as follows: 94 °C for 30 s, followed by 40 cycles of 94 °C for 5 s and 60 °C for 30 s, and with a final cycle of 95 °C for 10 s and 60 °C lasting for 5 s. The relative expression levels of the gene were calculated with the reference gene SmActin by the comparative CT method (2−ΔΔCt).

2.9. Target Prediction and Functional Annotation

We used TargetFinder (v1.6) software to predict the target of known miRNAs and potential miRNAs with default parameters; the threshold value of the matching score was set to 5.0. The nearest mRNAs in the range of 100 kb upstream and downstream of lncRNAs were identified as cis-target genes by Perl. We investigated the complementary sequence between lncRNAs and mRNAs with the LncTar tool and then calculated and standardized the free energy of the matching sites. Those mRNAs with a standardized free energy threshold < −0.1 were considered as the target genes of lncRNAs. The mRNAs with the highest matching degree with the circRNAs were selected as the parental genes, and the circRNAs were classified according to their position on the genome. Miranda (v3.3a) software was used to predict the target miRNA of the circRNA. TargetFinder software was used to predict the target mRNA of the target miRNA with default parameters (Expectation ≤ 5.0).
Gene function was annotated based on the following databases: Nr (NCBI non-redundant protein sequences); Nt (NCBI non-redundant nucleotide sequences); Pfam (protein family); KOG/COG (clusters of orthologous groups of proteins); Swiss-Prot (A manually annotated and reviewed protein sequence database); KO (KEGG ortholog database); GO (gene ontology).
GO enrichment analysis of the differentially expressed genes (DEGs) was implemented by the GOseq R packages. For targets of the differential expression lncRNAs and circRNAs, GO enrichment analysis of the DEGs was implemented by the topGO R packages. KOBAS was used to test the statistical enrichment of DEG targets of differential expression ncRNAs in KEGG pathways.

3. Results

3.1. Effective Constituent Contents in the White Root and Red Root

The HPLC results showed that the four main tanshinone components in the phloem and epidermis tissues of the red roots, including tanshinone IIA, tanshinone I, dihydrotanshinone I and cryptotanshinone, were significantly higher than those of the white roots (Table 1). In order to explore the molecular mechanism of tanshinone synthesis, differentially expressed profiles of miRNAs, lncRNAs, and circRNAs of these two kinds of roots were analyzed in this study.

3.2. High-Throughput RNAs Sequencing and Different Expression RNAs

A total of 27,723 genes were identified. Some 132 known miRNAs were identified, and 228 potential miRNAs were forecast (Supplementary Table S1). A total of 6929 candidate lncRNAs were identified. Furthermore, 6239 circRNAs were detected, of which 5633 (90.3%) were exonic circRNAs (ecircRNAs).
Based on the expression profiles of ncRNAs between the phloem of the red and white root, 731 genes, 70 miRNAs, 48 lncRNAs, and 26 circRNAs were discovered to be differentially expressed (DE) (Supplementary Table S2). Among them, 242 genes, 30 miRNAs, 26 lncRNAs, and 12 circRNAs were up-regulated, respectively in the phloem and epidermis of the red root rather than that of the white root; however, 489 genes, 40 miRNAs, 22 lncRNAs, and 14 circRNAs were down-regulated (Figure 1). Six DE-miRNAs and five DE-lncRNAs were selected at random to validate their expression by quantitative RT-PCR (Supplementary Figure S2). Experiment results indicated that qRT-PCR expression profiles of these ncRNAs were consistent with the results of the RNA-Seq, because that confirmed the effectiveness of RNA-Seq data.

3.3. Functional Annotation of DE-RNAs

In total, 661 DEGs, 1232 target genes of DE-miRNAs, 304 cis-target genes and 11 trans-target genes of DE-lncRNAs, as well as 12 source gene DE-circRNAs between the white and red root were annotated. The function annotation was integrated based on multiple databases, such as NR, Swiss-Prot, COG, KOG, Pfam (Table 2; Supplementary Table S3).

3.3.1. Functional Annotation of DEGs

In the KEGG metabolism pathway, galactose metabolism, phenylpropanoid biosynthesis, nitrogen metabolism, sesquiterpenoid and triterpenoid biosynthesis, phenylalanine metabolism, and monoterpenoid biosynthesis belong to the most significant pathways (Figure 2A), which reflect the distinct differences in the primary metabolism mainly in carbon/nitrogen and secondary metabolites biosynthesis mainly in phenylpropanoid and terpenoid. Besides the general function prediction only (R) and function unknown (S) in eggNOG annotation, secondary metabolites biosynthesis, transport and catabolism (Q), transcription (K), and carbodrate transport and metabolism (G) were the most DEGs (Figure 2B), which reflected secondary metabolites biosynthesis, carbodrate transport and metabolism on the transcriptome level with the biggest difference between the red root and the white root.
The up-regulated and down-regulated DEGs were analyzed respectively. Gene ontology classification and KEGG enrichment analysis were performed based on the cluster (Figure 3). In the three major biological fields of GO, more up-regulated DEGs in red roots were enriched in the biological processes. Among them, three more significant Go terms were cell wall organization, RNA biological population and transmembrane transport. In addition, the most significant Go term in the molecular function classification was the transmembrane transporter activity (Figure 3A). A large number of down-regulated DEGs were enriched in the molecular function. In this category, the three most significant GO terms were heme binding, iron ion binding and monooxygenase activity. Moreover, the extracellular region in the cellular component and oxidation-reduction process in the biological process were also the relatively significant Go terms (Figure 3B). The enrichment results of KEGG pathways show that a large number of DEGs were enriched in the metabolism pathway (Figure 3C,D). Among them, the up-regulated differentially expressed genes were significantly enriched to diterpenoid biosynthesis, nitrogen metabolism, biotin metabolism and ABC transporters (Figure 3C). The down-regulated differentially expressed genes were significantly enriched to galactose metabolism, phenylpropanoid biosynthesis, selenocompound metabolism, glucosinolate biosynthesis, cysteine and methionine metabolism, and nitrogen metabolism (Figure 3D).

3.3.2. Functional Annotation of Target Genes of DE-miRNAs

GO enrichment analysis revealed that 473 target genes of DE-miRNAs could be classified into 19 biological processes, 15 molecular functions, and 11 cellular component terms (Supplementary Table S4). For biological processes (BPs), the “lignin catabolic process,” “regulation of transcription, DNA-dependent,” and “auxin-activated signaling pathway” were the three most dominant categories. As for molecular functions (MFs), “hydroquinone: oxygen oxidoreductase activity” and “copper ion binding” were the two most significant terms. The three most dominant cellular components (CCs) were “apoplast,” “chloroplast stroma,” and “coated vesicle membrane” (Figure 4A). Based on the KEGG pathway analysis, 162 DE-miRNAs target genes were mainly involved in 58 metabolic pathways. Among them, the “Plant-pathogen interaction pathway,” “Biosynthesis of amino acids,” and “diterpenoid biosynthesis” pathway contain the most DEGs (Figure 3B; Supplementary Table S5).

3.3.3. Functional Annotation of Target Genes of DE-lncRNAs

Both the cis- and trans- targets of the DE-lncRNAs were analyzed. GO enrichment analysis displayed that 165 cis-regulated targets were significantly enriched. The most abundant BPs contained “protein retention in ER lumen,” “regulation of G2/M transition of the mitotic cell cycle,” and a “lipid metabolic process.” The most relevant MFs were “chromatin binding,” “oxidoreductase activity,” and “monodehydroascorbate reductase (NADH) activity.” In the CC group, targets were enriched in terms of “plant-type vacuole”, “peroxisome,” and “anchored component of membrane” (Figure 5A, Supplementary Table S4). The six trans-targets were correlated with 34 GO terms in three main classifications, including “membrane,” “organelle,” “binding and cellular process,” etc. (Figure 5C, Supplementary Table S4). KEGG analysis revealed the enrichment pathway of 111 cis-regulated targets that mainly included “ribosome, carbon metabolism, plant hormone signal transduction, glycerophospholipid metabolism, protein processing in the endoplasmic reticulum,” etc. The ribosome pathway is the most significant enrichment pathway (Figure 5B; Supplementary Table S5). There were only two KEGG pathways for two trans-regulated targets of DE-lncRNAs, “Proteasome” and “Ubiquinone and another terpenoid-quinone biosynthesis” (Figure 5D; Supplementary Table S5). Annotation results for other databases also showed that target genes of DE-lncRNAs encoded some receptor protein kinase, pentatricopeptide repeat-containing protein, peroxidase, membrane protein, transporter protein, zinc finger protein, hormone synthesis, and signal transduction-related protein.

3.3.4. Functional Annotation of Target Genes of DE-circRNAs

The significant functions of the source gene of DE-circRNAs were involved in the “acetyl-CoA metabolic process,” “purine ribonucleoside monophosphate metabolic process,” “glycosyl compound biosynthetic process,” “ATPase coupled ion transmembrane transporter activity,” “cation-transporting ATPase activity,” “FMN binding,” “bounding membrane of organelle,” “myosin complex,” “actin cytoskeleton,” etc. (Figure 6A, Supplementary Table S4). As a result of KEGG analysis, the target gene of DE-circRNAs was significantly enriched in only one pathway. The unigene in the alpha-linolenic acid metabolism pathway was analyzed between white phloem and red phloem samples (Figure 6B; Supplementary Table S5).

3.3.5. Functional Annotation of Target Genes of miRNAs Combined with DE-circRNAs

In addition, the 201 targets of 59 miRNAs combined with DE-circRNAs were also analyzed. Target genes were significantly enriched in BPs that contained a “defense response to the bacterium”, a “cellular biogenic amine metabolic process”, an “organic cyclic compound biosynthetic process”, a “purine nucleotide biosynthetic process”, and a “heterocycle biosynthetic process”. The enriched terms related to the MF included “sequence-specific DNA binding transcript,” “ligase activity,” “symporter activity,” “oxidoreductase activity,” and “oxidoreductase” (Figure 7C, Supplementary Table S4). The function of the DE target genes is mainly involved in 15 metabolic pathways, such as “Plant-pathogen interaction, Endocytosis, Phenylpropanoid biosynthesis, Porphyrin, and chlorophyll metabolism, Galactose metabolism.” Therefore, the “sesquiterpenoid and triterpenoid biosynthesis” pathway was paid close attention to. Only premnaspirodiene oxygenase (EVM0021656) was enriched in the pathway targeted by the unconservative_Lachesis_group2_8821 (Figure 5D; Supplementary Table S5).

3.4. Tanshinone Biosynthesis Pathway and Its ncRNA Regulation

In this study, all potential ncRNAs interacting with known key enzyme genes of the tanshinone synthesis pathway were predicted (Supplementary Table S6). As a consequence, five known genes were found that might be regulated by miRNAs. For example, geranylgeranyl diphosphate synthase (SmGGPPS1), the rate-limited enzyme in the pathway to synthesize tanshinone which can catalyze isoprenyl pyrophosphate (IPP) and farnesyl pyrophosphate (FPP) and then generate geranylgeranyl diphosphate (GGPP), was likely to be regulated by gma-miR168b. In total, 39 known genes in the tanshinone synthesis pathway might be cis-regulated by lncRNAs, and a few genes were regulated by multiple lncRNAs. Only ten known genes trans-regulated by lncRNAs were discovered. Among these, we predicted that several key rate-limiting enzyme genes were potentially cis-regulated as well as trans-regulated: SmAACT5, SmCMK, SmIPI1, SmGGPPS1, SmCPS5 and SmKSL1, for example. We found that 13 known key enzyme genes have circRNA interaction sites, such as SmAACT1, SmPMK, SmDXS2, SmDXS3, SmHDR1, and SmKSL2, and that they may be regulated by no fewer than two circRNAs. Furthermore, 101 known miRNAs and 270 potential miRNAs were likely to bind to circRNAs, and further be regulated. The expression profile of key enzyme genes related to the tanshinone synthesis pathway and some ncRNAs in the red root and white root are shown in Figure 7.

4. Discussion

4.1. A Large Number of ncRNAs Were Identified in Danshen

The content of tanshinone in red root of the same S. miltiorrhiza is high, while that in white root tissue is very little, which provides a new material for studying the biosynthesis and regulation of tanshinone. S. miltiorrhiza is an important medicinal plant, and the tanshinone and salvianolic acid in its roots has a remarkable curative effect on cardiovascular and cerebrovascular diseases [40,41]. Most ncRNAs in plants, which have preliminarily obtained functional clues, regulate multiple aspects of the development and response to environmental stress-specifics in tissue cells and exist in complex regulatory networks [42,43,44]. However, previous studies did not explore the expression characteristics of all ncRNAs in one experiment, especially the circRNA in Salvia miltiorrhiza. In this study, 132 conserved miRNAs, 228 potential miRNAs, 6929 lncRNAs and 6239 circRNAs were identified in S. miltiorrhiza, and their expressive characteristics were analyzed. All of these potential ncRNAs are likely to play different functions in the process of growth and development or resistance to stress, and will be confirmed in further studies. These results will provide a reference for the study of ncRNAs in the future.
In this study, we focused on analyzing the differentially expressed ncRNAs. A total of 70 miRNAs, 48 lncRNAs, and 26 circRNAs were found to be differentially expressed between the red and white roots, which indicated their potential regulatory roles in the biosynthesis of diterpenoid tanshinone. The number of differentially expressed ncRNAs was smaller than found in previous studies [45,46], which may result from the similar development period in the same genetic background. In addition, red roots and white roots only had a color difference on the epidermis, which led to the difference in tanshinone content, while no significant difference was found in other tissues. However, the root material used in this experiment included phloem and epidermis (the site of the tanshinone synthesis). The proportion of epidermis tissue in the whole phloem was small, which resulted in the fact that some differentially expressed ncRNAs were not significant and were not identified as DE ncRNAs. Therefore, in the future, only the epidermis with different contents should be taken to analyze the whole transcriptome.

4.2. NcRNAs Expressed Differentially in Root Tissues and Their Target Genes

In this study, the potential biological processes involving DEGs in red root and white root tissues were first analyzed and discussed. Our results indicate that the up-regulated DEGs in red roots were mainly enriched in some secondary metabolic pathways, such as phenylpropanoid biosynthesis and diterpenoid biosynthesis, while the enrichment pathways of up-regulated DEGs in white roots included some primary metabolic pathways and secondary metabolic pathways, such as galactose metabolism, starch and sucrose metabolism, and phenylpropanoid biosynthesis. The synthesis and accumulation of secondary metabolites in red roots and the up-regulation expression of related genes are the normal development processes of the S.miltiorrhiza root [47]. However, the white root seems to have experienced more primary biological activities, which suggested the abnormal development of the white root. In addition, the up-regulated DEGs in red roots were found to be significantly enriched in the diterpenoid biosynthesis pathway, which was also consistent with the accumulation of tanshinones [8,48]. In addition, the up-regulated DEGs in red roots were also found to be significantly enriched in the transmembrane transport activity and ABC transporters pathway, which may be related to the transmembrane transport of terpenoids and other secondary metabolites. It has been confirmed that some ABC transporters may be involved in the diterpene transport [49]. Three ABC genes (SmABCG46, SmABCG40 and SmABCG4) were co-expressed with the key enzymes of the tanshinone biosynthesis, which may be involved in the transport of tanshinones in root cells [50]. Our results can provide some valuable information for the study of tanshinone synthesis and metabolism.
To explore the potential regulatory functions of these differentially expressed ncRNAs, we carried out GO classification and KEGG enrichment analysis of their target genes. Studies have shown that the expression patterns of mRNA-like noncoding RNAs (mlncRNAs) exhibited tissue specificity, suggesting that mlncRNAs may be involved in regulating the growth and development of S.miltiorrhiza. In addition, many mlncRNAs were responsive to the treatment of elicitors (yeast extract, Ag+ and MeJA) which led to the production of bioactive compounds in S. miltiorrhiza, suggesting that lncRNAs may be involved in the production of active substances [51]. In this study, analysis of cis- and trans- target genes of differentially expressed lncRNAs revealed that most of the target genes were involved in the primary metabolism such as the ribosome, carbon metabolism and plant hormone signal transduction. Only one target gene was involved in the biosynthesis of ubiquinone and other terpene quinones. Our results indicated that lncRNAs may not directly target some key enzyme genes for secondary metabolite synthesis, but indirectly influence the synthesis of active substances by responding to the external environment.
The source genes of the circRNA and the interacting miRNAs were also analyzed. The source genes of the circRNA may be involved in some primary metabolic processes, such as the acetyl-coA metabolic process; target genes of miRNAs interacting with them participate in a defense response to the bacterium process and plant interaction pathway. At present, the functions of circRNAs in most plants are still unknown, especially in the regulation of secondary metabolites. Based on the expression patterns of the three ncRNAs in different tissues, it follows that miRNAs are more commonly and directly involved in the regulation of secondary metabolite synthesis than lncRNAs and circRNAs. Our results provide some references for future studies.
Combined with previous research results [22,51], tanshinone synthesis may be generally regulated by ncRNAs. In this paper, many ncRNAs were predicted that may interact with key enzyme genes of the tanshinone biosynthesis pathway, including SmDXS2, SmGGPPS1, and SmKSL. In addition, these ncRNAs were differentially expressed in the red root and the white root. It is implied that these ncRNAs may potentially regulate the synthesis and metabolism of a series of secondary metabolites, including tanshinone. These results provide a data basis and future research direction for ncRNAs to regulate the biosynthetic pathway of tanshinones. In a future study, we will validate the authentic interactions of these ncRNAs by constructing the knockout or overexpression transgenic plants of candidate ncRNAs in S.miltiorrhiza, and further observe the phenotype, exploring the expression difference of related genes and the content of tanshinones [52,53].
In addition, most target genes of these DE-ncRNAs still encoded some receptor protein kinase, zinc finger protein, disease-resistant proteins, F-box protein, hormone synthesis, and signal transduction-related protein. This means that these genes may be involved in plant and pathogenic interactions, which may be related to the abundance of microbial flora in the soil [54,55]. The pericarp of the root approached the soil directly and is closely stimulated by microorganisms, which process initiates the defense system of the root system [56]. Some studies suggest that secondary metabolites produced by plants are natural antimicrobial substances [57,58]. The potential regulatory relationships between the production of secondary metabolites and the interactions among plants and pathogens would be the focus of a future study. This study also provides new insights into the regulation mechanisms in the terpene synthesis process.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/agronomy13020321/s1, Figure S1: The red root and white root from the same S.miltiorrhiza plant after being grown for two months. The liquid in the sample bottle was the extract from the root of S.miltiorrhiza, which had grown for six months; Figure S2: Expression of lncRNAs (Right) and miRNAs (Left) in phloem and epidermis of red roots (RZP, black) and white roots (WZP, white) of a S. miltiorrhiza plants. Expression levels were quantified by qRT-PCR. The level of transcripts in RZP was arbitrarily set to 1, and the level in WZP was given relative to this. SmActin was used as the internal control gene, and three biological replicates were used; Table S1: Summary of known and predicted miRNA in this study.; Table S2: Expression profiles of DEGs and DE-ncRNAs in different tissues; Table S3: Functional annotation of target genes of DEGs and DE-ncRNAs; Table S4: Go enrichment analysis of targets of differentially expressed ncRNAs; Table S5: KEGG analysis of targets of differentially expressed ncRNAs; Table S6: NcRNAs that may regulate key enzyme genes of tanshinone biosynthesis and metabolism pathway.

Author Contributions

Conceptualization, Z.S. and X.L.; Data curation, C.L. and C.Z.; Formal analysis, Z.L.; Investigation, C.L. and C.Z.; Methodology, C.L. and Z.L.; Project administration, X.L.; Resources, Z.S. and X.L.; Software, C.Z.; Supervision, Z.S.; Visualization, C.L.; Writing—original draft, C.L.; Writing—review and editing, Z.S. and X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Agricultural Variety Project of Shandong Province (2021LZGC008), as well as the National Natural Science Foundation of China, grant number (81872949).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 13 00321 g001 550
Figure 1. Differential expression levels of 22 conserved miRNAs (A), 48 lncRNAs (B), 26 circRNAs (C) and 731 mRNAs (D) in two root tissues of S. miltiorrhiza. L05 and L06 represent the phloem and periderm of the red root; L09 and L10 represent the phloem and periderm of the white root. Red indicates a high expression of mRNAs and ncRNAs, and blue indicates a low expression of mRNAs and ncRNAs in the heatmaps.
Figure 1. Differential expression levels of 22 conserved miRNAs (A), 48 lncRNAs (B), 26 circRNAs (C) and 731 mRNAs (D) in two root tissues of S. miltiorrhiza. L05 and L06 represent the phloem and periderm of the red root; L09 and L10 represent the phloem and periderm of the white root. Red indicates a high expression of mRNAs and ncRNAs, and blue indicates a low expression of mRNAs and ncRNAs in the heatmaps.
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Figure 2. KEGG pathway (A) and eggNOG function classification (B) enriched in DEGs between phloems of white root and red root.
Figure 2. KEGG pathway (A) and eggNOG function classification (B) enriched in DEGs between phloems of white root and red root.
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Figure 3. The Go and KEGG pathway classification of the DEGs in the red root vs. the white root. Top 20 Go terms with the most significant enrichment of the up-regulated (A) and down-regulated (B) DEGs in red root. The abscissa represents −log10 (p value), and the ordinate represents the GO function. The numbers close to the bars represent the numbers of genes belonging to each category. The scatter diagram of KEGG pathway enrichment of up-regulated (C) and down-regulated (D) DEGs in the red root. The abscissa represents the enrichment factor, and the ordinate represents the −log10 (Q value).
Figure 3. The Go and KEGG pathway classification of the DEGs in the red root vs. the white root. Top 20 Go terms with the most significant enrichment of the up-regulated (A) and down-regulated (B) DEGs in red root. The abscissa represents −log10 (p value), and the ordinate represents the GO function. The numbers close to the bars represent the numbers of genes belonging to each category. The scatter diagram of KEGG pathway enrichment of up-regulated (C) and down-regulated (D) DEGs in the red root. The abscissa represents the enrichment factor, and the ordinate represents the −log10 (Q value).
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Figure 4. The gene ontology (GO, (A)) and the Kyoto encyclopedia of genes and genomes (KEGG, (B)) analysis of DE-miRNAs targets in two root tissues of S. miltiorrhiza.
Figure 4. The gene ontology (GO, (A)) and the Kyoto encyclopedia of genes and genomes (KEGG, (B)) analysis of DE-miRNAs targets in two root tissues of S. miltiorrhiza.
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Figure 5. The gene ontology (GO, (A)) and the Kyoto encyclopedia of genes and genomes (KEGG, (B)) analysis of cis-targets of DE-lncRNAs, the GO (C) and KEGG (D) analysis of trans-targets of DE-lncRNAs in two root tissues of S. miltiorrhiza.
Figure 5. The gene ontology (GO, (A)) and the Kyoto encyclopedia of genes and genomes (KEGG, (B)) analysis of cis-targets of DE-lncRNAs, the GO (C) and KEGG (D) analysis of trans-targets of DE-lncRNAs in two root tissues of S. miltiorrhiza.
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Figure 6. The gene ontology (GO, (A)) and the Kyoto encyclopedia of genes and genomes (KEGG, (B)) analysis of DE-circRNAs targets, the GO (C) and KEGG (D) analysis of targets of miRNAs combined with DE-circRNAs in two root tissues of S. miltiorrhiza.
Figure 6. The gene ontology (GO, (A)) and the Kyoto encyclopedia of genes and genomes (KEGG, (B)) analysis of DE-circRNAs targets, the GO (C) and KEGG (D) analysis of targets of miRNAs combined with DE-circRNAs in two root tissues of S. miltiorrhiza.
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Figure 7. The pathways and genes involved in the biosynthesis of tanshinones in Salvia miltiorrhiza. The heat map on both sides of the pathway shows that the expression profiles of key enzyme genes of tanshinone biosynthesis pathway in red root and white root are based on RNA-Seq analysis. Colored cells on the left or right of the main heatmap are the expression profiles of some ncRNAs, which may interact with some key enzyme genes. Red indicates high expression of genes and ncRNAs, and blue indicates low expression of genes and ncRNAs in the heatmaps.
Figure 7. The pathways and genes involved in the biosynthesis of tanshinones in Salvia miltiorrhiza. The heat map on both sides of the pathway shows that the expression profiles of key enzyme genes of tanshinone biosynthesis pathway in red root and white root are based on RNA-Seq analysis. Colored cells on the left or right of the main heatmap are the expression profiles of some ncRNAs, which may interact with some key enzyme genes. Red indicates high expression of genes and ncRNAs, and blue indicates low expression of genes and ncRNAs in the heatmaps.
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Table
Table 1. The content of effective constituents in white roots and red roots in same plantlet.
Table 1. The content of effective constituents in white roots and red roots in same plantlet.
Content (mg/g)Phloem in Red RootXylem in Red RootPhloem in White RootXylem in White RootWavelength
Detected (nm)
Tanshinone IIA4.340.00100269
Dihydrotanshinone0.50.00100269
Cryptotanshinone1.510.0020.001 269
Tanshinone I0.9680.00100269
Table
Table 2. Statistical table of the number of differentially expressed ncRNA target genes and source genes.
Table 2. Statistical table of the number of differentially expressed ncRNA target genes and source genes.
TypeAnnotatedCOGGOKEGGKOGSwissproteggNOGNr
DE-mRNAs66126036220433254320540
Targets of DE-miRNAs12323754733156778941116143
cis-targets of DE-LncRNAs30412516511118421000
tran-targets of DE-LncRNAs112639500
source genes of DE-circRNAs1259359120
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Lin, C.; Zhou, C.; Liu, Z.; Li, X.; Song, Z. Identification of the Genome-Wide Expression Patterns of Non-Coding RNAs Associated with Tanshinones Synthesis Pathway in Salvia miltiorrhiza. Agronomy 2023, 13, 321. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13020321

AMA Style

Lin C, Zhou C, Liu Z, Li X, Song Z. Identification of the Genome-Wide Expression Patterns of Non-Coding RNAs Associated with Tanshinones Synthesis Pathway in Salvia miltiorrhiza. Agronomy. 2023; 13(2):321. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13020321

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Lin, Caicai, Changhao Zhou, Zhongqian Liu, Xingfeng Li, and Zhenqiao Song. 2023. "Identification of the Genome-Wide Expression Patterns of Non-Coding RNAs Associated with Tanshinones Synthesis Pathway in Salvia miltiorrhiza" Agronomy 13, no. 2: 321. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13020321

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