Next Article in Journal
Agro-Based Spent Mushroom Compost Substrates Improve Soil Properties and Microbial Diversity in Greenhouse Tomatoes
Next Article in Special Issue
Identification and Evaluation of Diterpenoids from Glandular Trichome Secretions of Air/Sun-Cured Tobacco Germplasm Resources
Previous Article in Journal
Determination of Gingerols and Shogaols Content from Ginger (Zingiber officinale Rosc.) through Microwave-Assisted Extraction
Previous Article in Special Issue
Identification and Analysis of SOD Family Genes in Peanut (Arachis hypogaea L.) and Their Potential Roles in Stress Responses
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 13
Issue 9
10.3390/agronomy13092290
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Integrated Transcriptomics and Metabolomics Analysis Reveal Anthocyanin Biosynthesis for Petal Color Formation in Catharanthus roseus

by
Yuchen Xiao
,
Yueli Tang
,
Xianhui Huang
,
Lingjiang Zeng
* and
Zhihua Liao
*
Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City, School of Life Sciences, Southwest University, Chongqing 400715, China
*
Authors to whom correspondence should be addressed.
Agronomy 2023, 13(9), 2290; https://doi.org/10.3390/agronomy13092290
Submission received: 30 July 2023 / Revised: 28 August 2023 / Accepted: 28 August 2023 / Published: 30 August 2023
(This article belongs to the Special Issue Advances in the Industrial Crops)
Browse Figures
Review Reports Versions Notes

Abstract

:
Catharanthus roseus exhibits vibrant petals and displays robust resistance to disease and drought, making it highly valuable for ornamental and gardening applications. While the application of C. roseus as a source of anticancer drugs has gained considerable attention in recent years, there has been limited investigation into the regulatory mechanism underlying anthocyanin accumulation in the petals of C. roseus. This study comprehensively analyzed the metabolome and transcriptome of three distinct C. roseus varieties exhibiting different petal colors. Out of the 39 identified flavonoids, 10 anthocyanins exhibited significant variations in accumulation, directly contributing to the diverse coloration of C. roseus petals. Among them, malvidin 3-O-glucoside and petunidin 3-O-glucoside were identified as primary contributors to the purple petal phenotype, while peonidin 3-O-glucoside and delphinidin 3-O-glucoside exhibited the highest contribution rates to the red petals. Additionally, the variation content of cyanidin 3-O-rutinoside, delphinidin 3-O-glucoside, and petunidin 3-O-rutinoside also influenced the color transformation of C. roseus petals. RNA sequencing identified a total of 4173 differentially expressed genes (DEGs), including 1003 overlapping DEGs. A combined transcriptome and metabolome analysis showed that the coordinately regulated anthocyanin biosynthetic genes including chalcone isomerase (CHS), flavonoid 3′-hydroxylase (F3′H), and dihydroflavonol 4-reductase (DFR) played critical roles in the formation of the anthocyanins. MYB and bHLH transcription factors were also found to be significantly correlated with differences in flower color. These results serve as a foundation for future investigations into anthocyanin biosynthesis and regulatory mechanisms in C. roseus.

1. Introduction

Catharanthus roseus, belonging to the Apocynaceae family, is a perennial herb or subshrub within the genus Catharanthus. This plant serves as a model organism for investigating the biosynthesis pathway of monoterpenoid indole alkaloids (MIAs), known for its diverse components of MIAs [1]. In recent years, C. roseus has garnered significant attention due to its remarkable medicinal value as the exclusive plant source of the anticancer drugs vinblastine and vincristine [2]. Furthermore, its resistance to disease and drought, strong adaptability, and diverse range of colors contribute to its high value for ornamental and gardening applications. Nevertheless, this aspect has received relatively less attention. Notably, the vibrant flowers and diverse colors of C. roseus hold significant commercial value in garden landscaping.
Flower color, being the primary characteristic of ornamental plants, is closely linked to pigment type and distribution. Currently known natural pigments predominantly include flavonoids, carotenoids, and alkaloids [3,4,5]. Among them, flavonoids have attracted increasing attention as crucial secondary metabolites in plants. Flavonoids play important roles in diverse plant physiological processes, encompassing ultraviolet protection, insect attraction, pathogen defense, and influencing plant color variation [6]. Flavonoids are biosynthesized via a branch of the phenylpropanoid metabolic pathway and have been well characterized in Arabidopsis and petunia [7,8]. Flavonoids can be categorized into various subclasses, such as anthocyanin, flavone, flavonol, isoflavone, flavanol, and flavanone [9]. As a soluble pigment, anthocyanins possess unparalleled advantages in terms of antioxidant capacity, which scavenge free radicals, compared with other food pigments [10]. In recent years, numerous studies on anthocyanins have also elucidated their important role in coloring plant organs. For instance, anthocyanins were involved in the formation of purple leaf color in Populus deltoides and Ziziphus jujube [11,12]. The change of color in tobacco and miniature roses was affected by the corresponding anthocyanins [13,14]. Additionally, anthocyanins also contribute to the fruit color, such as Kiwifruit pulp and Fragaria × Ananassa [15,16]. Anthocyanin biosynthesis is a branch of the flavonoid synthesis pathway. The flavonoid pathway commences with chalcone synthase (CHS), which is responsible for the synthesis of naringenin chalcone from 4-coumaryl-coA. Subsequently, chalcone isomerase (CHI) catalyzes the isomerization of naringenin chalcone to naringenin. Naringenin is converted to dihydrokaempferol (DHK) by flavonoid 3-hydroxylase (F3H). DHK can be further hydroxylated to dihydroquercetin (DHQ) or dihydromyricetin (DHM) by flavonoid 3′-hydroxylase (F3′H) or flavonoid 3′,5′-hydroxylase (F3′5′H). Following this, dihydroflavonol 4-reductase (DFR) serves as the initial step in anthocyanin biosynthesis. Depending on the plant species, it can employ any or all of the three potential dihydroflavonols as the substrate, resulting in the formation of the respective leucocyanidin which shape the basic skeleton for anthocyanin biosynthesis. Leucoanthocyanidins are then converted into their corresponding anthocyanidins through the enzymatic activity of leucoanthocyanidin dioxygenase/anthocyanidin synthase (LDOX/ANS) [17]. Ultimately, anthocyanins undergo modification through various glycosyltransferases, resulting in their glycosylation into the corresponding glycosides or methylation to produce malvacin, peonidin, petunian glycosides, and other derivatives [18]. Concurrently, the biosynthesis of anthocyanins is governed by transcription factors (TFs), such as MYB, bHLH, and WD40 [19,20,21,22,23]. These TFs interact to form the MYB-bHLH-WD40 (MBW) protein complex, which regulates anthocyanin synthesis [24].
The vibrant color exhibited by C. roseus undoubtedly offers a valuable resource for investigating the genetic and molecular mechanisms underlying flower coloration. Currently, a systematic investigation on the formation and mechanisms of flower coloration in C. roseus is lacking. In this study, we integrated metabolomics and transcriptome analysis to detect and quantify the composition of flavonoids, especially anthocyanidin, in C. roseus petals. Additionally, we identified candidate genes involved in the coloration mechanism in C. roseus, elucidated the regulatory network governing anthocyanin biosynthesis, and established a foundation for metabolic engineering of anthocyanin biosynthesis in C. roseus petals. At the same time, we hope to provide new ideas for the innovative breeding of new flower colors.

2. Materials and Methods

2.1. Collection of Plant Materials

Three C. roseus varieties were used in the test. The formal identification of plant material was carried out by Professor Zhihua Liao. The cultivation of the plants took place at Jiulongpo National Biological Industry Park, located in Chongqing, China (29°35′ N, 106°33′ E). Subsequently, they were stored at the Key Laboratory of Resource Plant Protection and Germplasm Innovation of Chongqing. Petal samples were collected to conduct metabolomics and RNA-Seq analysis, with three biological replicates established. The most uniform and representative part of fresh petals were taken to determine the type of color in the indoor natural scattering light near the north (prevent sun direct illuminate) using the Royal Horticultural Society Color Chart (RHSCC) [25].

2.2. Measurement of Total Anthocyanin Content

Fresh petals were collected to determine the total anthocyanin content following the method outlined by Fu et al. [26]. We weighed 100 mg of C. roseus petals and ground them into powder in liquid nitrogen. Subsequently, 5 mL of 95% ethanol (0.1 mol L−1 HCl) was added, and the mixture was extracted for 1 h at 60 °C and 2200 rpm using shaking. A spectrophotometer was used to measure the absorption of the petals extracts at wavelengths of 520 nm, 620 nm, and 650 nm using 95% ethanol (0.1 mol L−1 HCl) as the blank control. The total anthocyanin content was quantified using the formula Q = A × V × 1000/489.72 M (mmol/g FW), where A = (A530 − A620) − 0.1 (A650 − A620). In the formula, V represents the volume of the extraction liquid and M denotes the mass of the fresh sample.

2.3. Extraction, Identification, and Quantitative Analysis of Metabolites

Fresh petals were collected and placed in the centrifuge tube and immediately frozen by liquid nitrogen. The samples were thoroughly ground using a plant material grinder. Then, 100 mg of the powder was weighed and mixed with 1.0 mL of 70% methanol aqueous solution. Extraction was conducted at 16 °C and 2200 rpm for 3 h, followed by centrifugation at 10,000× g rpm for 10 min. The supernatant was filtered into the injection vial using a filter head with a pore size of 0.22 μm. The extracts were analyzed using Thermo Fisher’s liquid chromatography-mass spectrometry instrument, liquid system VanquishTM Flex UHPLC, and mass spectrometry system Orbitrap ExplorisTM 120. A 3 microliter volume of extract was injected into a HPLC system on a C18 column (Waters ACQUITY UPLC HSS T3, 1.8 μm, 2.1 mm × 150 mm). See Supplementary Materials for related parameters of system settings (Tables S1–S3). Metabolites were qualitatively and quantitatively analyzed using Compound Discoverer 3.0, based on self-built database and public metabolite databases accessed on April 2023, namely, PubChem (https://pubchem.ncbi.nlm.nih.gov/, accessed on 29 July 2023), HMDB (http://www.hmdb.ca/, accessed on 29 July 2023), and METLIN (http://metlin.scripps.edu/index.php, accessed on 29 July 2023). Qualitative analysis of primary and secondary mass spectrometry data was performed by comparing accurate precursor ion (Q1), product ion (Q3) values, and retention time (RT). No real standard compounds were used. The peak areas of each identified compound were employed for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The screening criteria for identifying differentially accumulated flavonoids (DAFs) and differentially accumulated anthocyanins (DAAs) were set as fold change ≥ 2 and fold change ≤ 0.5, along with a project variable importance (VIP) score ≥ 1.

2.4. RNA Extraction and Sequencing

The lyophilized petal material was ground at low temperature, followed by extraction using the Trizol kit. The purity of the RNA was assessed using Nanodrop. The RNA concentration and total amount were accurately determined using Qubit. RNase-free agarose gel electrophoresis was used to detect RNA fragment distribution. mRNA was enriched using mRNA Capture Beads (oligo dT beads), the fragmented RNA was reversely transcribed into cDNA, and the cDNA end was repaired with dA-tailing and connected to the sequencing adapter. Magnetic beads were used for purification or sorting, followed by PCR amplification. The concentration and fragment size distribution of the PCR products were assessed, and pooling was carried out based on the desired data volume. Subsequently, sequencing was conducted using the NovaSeq 6000 high-throughput. All processes were completed by Wuhan Benagen Technology Company Limited (Wuhan, China).

2.5. Analysis and Annotation of RNA-Seq Data

The reads acquired from the sequencing platform underwent filtration using fastp to obtain high-quality clean reads suitable for assembly and analysis. The clean reads were subsequently aligned to the reference genome published by Chen et al. for Catharanthus roseus cultivar: SunStorm Apricot Madagascar C. roseus (Accession: PRJNA847226 ID: 847226) [1]. The mapped reads were assembled using StringTie v1.3.1. The expression levels were quantified using the trimmed mean of m-values (TMM). All transcripts underwent annotation using the Gene Ontology (GO) database, Kyoto Encyclopedia of Genes and Genomes (KEGG) database, NCBI non-redundant (Nr) database, Swiss-Prot protein database, and Pfam database. Genes meeting the criteria of a p-value ≤ 0.05, fold change ≥ 2, and fold change ≤ 0.5 were identified as differentially expressed genes (DEGs). The DEGs among the three group samples were identified by edgeR for subsequent analysis.

2.6. Structural Gene and Transcription Factor Analysis

The Hidden Markov Model (HMM) was constructed for the anthocyanidin synthesis pathway of C. roseus, incorporating related structural genes (e.g., PAL, 4CL, DFR, ANS) and transcription factors (e.g., MYB, bHLH, WD40), using Hmmbuild. Subsequently, homologous genes were identified through Hmmsearch [27]. DEGs were selected for the analysis of both structural genes and transcription factors. Spearman correlation analysis was conducted between the content of DAFs and the datasets of DEGs. If the Spearman correlation coefficient |r| > 0.9 and p values are significantly < 0.05 then it suggests differentially expressed strong correlation between the DAFs and DEGs.

2.7. Quantitative Real Time PCR and Expression Validation

In order to validate the RNA Seq results, qRT-PCR was performed on the selected candidate genes to identify their expression levels in flowers of different colors. Specific primers for the 10 selected genes were designed by the National Center of Biotechnology Informationand are listed (Table S4), while the 40S ribosomal protein S9 (RPS9) gene was used to assess the expression of a particular gene [28]. The PCR program was: 95 °C for 10 min, 39 cycles of 95 °C for 10 s, 55 °C for 30 s, and 72 °C for 30 s. The reactions were repeated three times in all experiments, and the 2−ΔΔCt method was used to calculate the relative expression levels of target genes.

3. Results

3.1. Total Anthocyanin Content in Petals with Different Colours

The appearance of the red flower and purple flower, referred to as CR and CP, exhibited a greater abundance of pigments compared to the white flower, referred to as CW (Figure 1a).By comparison with the RHSCC colorimeter, the color standard of CW, CP, and CR are NN155D, N81B, and N57A separately (Table S5). To gain insight into the metabolic mechanism of the color phenotype in C. roseus petals, the total anthocyanin content in the petals was quantified. The contents of total anthocyanin in CR and CP were 139.37 mM g−1 and 111.49 mM g−1, respectively, which was significantly higher than that in CW. There was no significant difference between CR and CP. The total anthocyanin content of CW was only 6.78 mM g−1 (Figure 1b). These results corresponded with the color intensity of the C. roseus petals.

3.2. Metabolite Difference Analysis

Petals from CW, CR, and CP were collected and analyzed to determine their metabolite concentrations. A total of 39 DAFs were identified among the different varieties. These flavonoids were categorized into 7 groups, comprising 11 flavonols, 10 anthocyanins, 9 flavonones, 6 flavones, 1 isoflavone, 1 isoflavonol, and 1 chalcone (Table 1). PCA analysis demonstrated the high repeatability of the samples, allowing for further analysis (Figure 2a). The relative content of all DAFs was analyzed (Figure 2b). The Venn diagram of the three groups revealed the presence of 21 compounds expressed in all groups (Figure 2c). We conducted pairwise comparisons of the compounds, and the most significant changes in metabolite levels were observed in CR, showing both increased and decreased abundances. To identify the compounds responsible for petal coloration, the white flower CW was used as the reference group. The results indicated that there were 15 and 7 types of DAFs that were upregulated in CP and CR, respectively, which might play a crucial role in influencing petal coloration (Figure 2d). Subsequently, we identified the overlapping metabolites, revealing that 11 DAFs were common to CR vs. CW, CP vs. CW, and CP vs. CR. The majority of these DAFs belong to the flavonol and anthocyanin classes (Figure 2e).
Special attention was paid to the differences in anthocyanin among CW, CR, and CP, as anthocyanin plays a crucial role in flower color change [29,30,31]. A hierarchical heatmap clustering analysis was conducted using the anthocyanidin compound contents of CW, CR, and CP (Figure 3a). Interestingly, the three groups exhibited distinct separation, indicating different anthocyanidin profiles in different color petals. CR exhibited significantly higher levels of delphinidin 3, 5-diglucoside, delphinidin-3-O-glc-1-3-rham-1-6-glucoside, delphinidin 3-O-glucoside, and peonidin 3-O-glucoside compared to the other groups. Malvidin 3-O-glucoside was exclusively detected in CP samples while only peonidin 3-O-glucoside was not detected in CP samples, indicating their potential as a crucial anthocyanin contributing to the different colors of CP and CR. Additionally, we determined the overlapping anthocyanin content among the three samples. The analysis found that the content of cyanidin 3-O-rutinoside, delphinidin 3-O-glucoside, and petunidin 3-O-rutinoside varied greatly among CW, CR, and CP (Figure 3b).

3.3. Overview of Full Transcriptome Sequencing Result

To further study the regulatory mechanisms of C. roseus petal coloring, RNA-seq analysis was performed. Nine cDNA libraries were established using the three varieties of samples (three biological replicates for each variety) for metabolite analysis to enable high-throughput RNA-seq. A total of 22,233,741 to 24,706,151 raw reads for each sample were obtained by sequencing. Clean reads were obtained after filtering low-quality reads and accounted for 99.25–99.59% of the raw data. The Q20 and Q30 values of each library were greater than or equal to 96.89% and 93.48%, respectively (Table S6).

3.4. Differentially Expressed Genes

Sequence and expression information was obtained for 26,348 genes to facilitate further analysis. The correlation coefficients (r) based on the TMM values were calculated to assess the relationship among the three samples. The majority of correlation coefficients (r) between the biological replicates of the three samples exceeded 0.9, indicating high similarity (Figure 4a). The volcano plot of DEGs demonstrated the dispersion of samples across the four distinct groups, with clustering within the same group. This observation supports the suitability of biological replicates for detecting DEGs (Figure 4b,c). To identify DEGs between CW, CP, and CR, a total of 4173 DEGs were characterized among all samples by analyzing the TMM value of transcripts obtained from the transcriptome data, using a criteria of |log2(FoldChange)| ≥ 1 and a p-value ≤ 0.05. Specifically, 3172 DEGs (2111 up-regulated and 1061 down-regulated) were identified for the CR vs. CW comparison, 1701 DEGs (937 up-regulated and 764 down-regulated) for the CP vs. CW comparison, and 1029 DEGs (619 up-regulated and 410 down-regulated) for the CR vs. CP comparison (Figure 4d). Furthermore, the overlapping DEGs between the CW vs. CP and CW vs. CP groups were determined using the Venn diagram function (Figure 4e). A total of 1003 genes were found to be differentially expressed in both groups, suggesting their potential key functions in influencing the coloration of different petals.
KEGG annotation and enrichment analysis were conducted for the three comparison groups: CR vs. CW, CP vs. CW, and CR vs. CP. The majority of DEGs were annotated to metabolic pathways, particularly carbohydrate metabolism, amino acid metabolism, and biosynthesis of secondary metabolites, across all three groups (Figure S1). The results of the KEGG enrichment analysis were ranked based on their respective p-values. Flavonoid biosynthesis and phenylpropanoid biosynthesis were among the top 25 enriched pathways in all three groups, which indicated that these pathways showed high significance in the enrichment analysis (Figure 5a,b).

3.5. The Candidate Genes Involved in Anthocyanin Biosynthesis Pathway

To investigate the variations in anthocyanin biosynthesis among CW, CR, and CP, we identified differentially expressed genes (DEGs) in the anthocyanin synthesis pathway. Specifically, we identified 18 DEGs associated with the regulation of petal colors. The identified DEGs included 4CL, CHS, CHI, F3′H, F3′5′H, DFR, and ANS. Additionally, BZ1, UGT75C1, and MT were also identified, potentially involved in methylation and glycosylation processes following anthocyanin synthesis (Figure 6). Among the DEGs involved in anthocyanin biosynthesis, 4CL and ANS exhibited significant upregulation in both CR and CP, consistent with the high anthocyanin contents in red and purple petals. F3′H and F3′5′H were exclusively upregulated in red petals, indicating their pivotal roles in the red flower coloration. However, CHI was notably upregulated in white flowers, contrary to the total anthocyanin content in white petals. Additionally, we identified an upregulated MT, potentially contributing to the formation of malvidin and petunidin.

3.6. Transcription Factors Related to Anthocyanin Biosynthesis

As we all know, the R2R3-MYB transcription factor, bHLH transcription factor, and WD40 repeat protein play an essential role in regulating anthocyanin biosynthesis. Given the up-regulated anthocyanin accumulation in CR and CP, we focused on the significantly up-regulated transcription factor genes MYB, bHLH, and WD40 (fold change ≥ 2, and fold change ≤ 0.5). The CR_vs_CW comparison group exhibited differential expression in 24 MYB, 23 WD40, and 2 bHLH (Figure 7a). In the CP_vs_CW comparison group, there was differential expression of 14 MYBs, 8 WD40s and 2 bHLHs (Figure 7b). We found 24 MYBs, 10 WD40s, and 1 bHLH were differentially expressed in the CR_vs_CP comparison group (Figure 7c). Furthermore, 4 MYBs, 2 bHLHs, and 2 WD40s were significantly upregulated in CP_vs_CW and CR_vs_CW comparisons, respectively.

3.7. Integrated Transcriptome and Metabolome Analysis

To further explore the relationship between anthocyanin genes and differentially accumulated anthocyanins, a heat map of gene-metabolite correlation was performed (Figure S2). Most of the anthocyanins had significant correlations with the MYB gene (CRO_03G031380). The combined analysis results indicated that delphinidin-3-O-glc-1-3-rham-1-6-glucoside, and delphinidin 3,5-diglucoside had significant correlations with CHS (CRO_06G025530). Malvidin 3-O-glucoside and petunidin 3-O-glucoside had a significant negative correlation with FLS (CRO_01G023220) (Figure 8a). FLS (CRO_06G000510) had a significant negative correlation with bHLH (CRO_07G004820) and F3′5′H (CRO_04G034940) had a significant correlation with WD40 (CRO_S000630) (Figure 8b).

3.8. Verification of the Results in RNA-Seq by qRT-PCR

To validate the accuracy of the RNA-seq data, 10 genes related to anthocyanin biosynthesis were selected for qRT-PCR. The relative expression levels of these 10 genes were normalized to the expression of RPS9 gene. The results showed a similar expression pattern of the qRT-PCR level to those genes obtained in the RNA-seq data (Figure S3), indicating that the RNA-seq analysis results were reliable, which would be helpful for further study of anthocyanin biosynthesis among differently colored flowers.

4. Discussion

4.1. Effects of Anthocyanin Content and Types on the Catharanthus roseus Petals

Although there has been progress in understanding the molecular mechanisms of plant color formation [32,33], the regulation of pigments still exhibits species-specific characteristics. The coloration of plants is influenced by a combination of environmental factors and genetic influences [34]. In this study, both CR and CP samples exhibited a high total anthocyanin content, which correlated with the intensity of the flower’s color, suggesting that a higher quantity of anthocyanin contributes to petal coloration. The CR samples showed higher metabolic activity, characterized by the largest number of differentially accumulated metabolites, indicating a more intricate influence of factors on the coloration of red flowers. Our attention has been directed towards investigating the impact of anthocyanins on petal pigmentation. The C. roseus petals contained four delphinidin, three cyanidin, two petunins, one peonidin, and one malvidin. Notably, pelargonidin was not detected, suggesting its lack of contribution to petal coloration in this species.
The predominant compounds in red petals were peonidin 3-O-glucoside and delphinidin 3-O-glucoside, while malvidin 3-O-glucoside and petunidin 3-O-glucoside were primarily detected in purple petals. These findings are consistent with the review conducted by Iwashina [35]. The exclusive presence of malvidin 3-O-glucoside in CP suggests its potential role as a determinant in the transition from red to purple petal color [36]. In our present experiment, an increase in delphinidin content led to red petal coloration, possibly due to the synergistic effect with peonidin 3-O-glucoside, which has a high content in CR but was not detected in CP [37,38].

4.2. Key Structural Genes Responsible for Anthocyanin Synthesis in Catharanthus roseus Petals

In this study, the synthetic genes 4CL, CHS, and ANS were up-regulated in CR and CP. These expression patterns are consistent with previous research [39,40,41,42,43]. It is noteworthy that the expression patterns of the two identified DFR genes were completely opposite. However, DFR (CRO_04G031350) exhibited a high background expression level in CW, CR, and CP, indicating that even if the expression level in CR and CP is slightly lower, it can still facilitate the accumulation of ANS substrates. Luo et al. [44] proposed a model in which FLS and DFR compete for common substrates by heterologous overexpression of FLS gene in tobacco and validation of transgene inhibition of DFR expression. Many similar results confirmed this model and revealed the molecular mechanism of red or white flower formation [45,46]. In this study, there is also such a situation. In this study, there is also such a situation. FLS was observed high expression in CW, which caused the transformation from DHK to the flavonol pathway. The substrate DHK to anthocyanin synthesis pathway of CW was significantly reduced, and the decreased DHK level resulted in the decline of anthocyanin accumulation, causing the formation of white petals. A small amount of DHK entered the anthocyanin synthesis pathway, which was converted to cyanidin, and was perhaps responsible for the formation of the red spot in the center of white petals. Zuo Li et al. [47] overexpressed a bifunctional F3′5′H gene in tobacco, leading to the conversion of DHK to, respectively, form DHQ and DHM, resulting in a change in flower color from light pink to various purple phenotypes. However, in C. roseus, the expression levels of F3′H and F3′5′H genes in CR are the highest, and both peonidin 3-O-glucoside and delphinidin 3-O-glucoside are significantly accumulated in red petals. This observation may suggest that the formation of red petals in C. roseus requires a proportional accumulation of peonidin 3-O-glucoside and delphinidin 3-O-glucoside. Based on the above results, we also speculate that F3′H/F3′5′H and FLS compete for the same substrate DHK in the anthocyanin synthesis pathway of C. roseus, thereby promoting the upregulation of the downstream gene DFR in colored petals. Furthermore, the presence and high expression of F3′H in flowers can remove the substrate for production of pelargonidin, which is consistent with our experimental result that pelargonidin was not detected [48].

5. Conclusions

In this study, metabolomics and transcriptomics analyses were performed to reveal the differential regulation of anthocyanin biosynthesis in differently colored C. roseus flowers (CW, CR, and CP). A total of 39 differential accumulated flavonoids were detected, including 10 anthocyanins. Peonidin 3-O-glucoside and malvidin 3-O-glucoside may be the main components that determine the red or purple color of C. roseus, respectively. We identified 18 DEGs in the anthocyanin biosynthesis pathway of C. roseus. A model in which FLS competes with F3′H/F3′5′H for the same substrate DHK in the anthocyanin synthesis pathway of C. roseus was proposed. Our findings contribute valuable information and provide new insight for the evaluation of genetic diversity in C. roseus.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13092290/s1, Table S1: Mobile phase setup for liquid chromatography; Table S2: Injection time and flow rate; Table S3: Mass spectrum setup; Table S4: Primers used in qRT-PCR; Table S5: Petals color determining; Table S6: Summary of sequencing data; Figure S1: KEGG gene annotation results in different groups; Figure S2: Heatmap of metabolite abundance according to hierarchical clustering analysis; Figure S3: qRT-PCR validation of gene expression level in the transcriptome.

Author Contributions

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

Funding

This research project (202210635047) is finacially supported by the National Training Program of Innovation and Entrepreneurship for Undergraduates.

Data Availability Statement

The data that support the findings of this study are available from the authors upon reasonable request.

Acknowledgments

We would like to thank State Key Laboratory of Resource Insects for Provisioning of servers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Li, C.; Wood, J.C.; Vu, A.H.; Hamilton, J.P.; Rodriguez Lopez, C.E.; Payne, R.M.E.; Serna Guerrero, D.A.; Gase, K.; Yamamoto, K.; Vaillancourt, B.; et al. Single-Cell Multi-Omics in the Medicinal Plant Catharanthus roseus. Nat. Chem. Biol. 2023, 19, 1031–1041. [Google Scholar] [CrossRef]
  2. Kumar, S.; Singh, B.; Singh, R. Catharanthus roseus (L.) G. Don: A Review of Its Ethnobotany, Phytochemistry, Ethnopharmacology and Toxicities. J. Ethnopharmacol. 2022, 284, 114647. [Google Scholar] [CrossRef]
  3. Wang, L.S.; Shiraishi, A.; Hashimoto, F.; Aoki, N.; Shimizu, K.; Sakata, Y. Analysis of Petal Anthocyanins to Investigate Flower Coloration of Zhongyuan (Chinese) and Daikon Island (Japanese) Tree Peony Cultivars. J. Plant Res. 2001, 114, 33–43. [Google Scholar] [CrossRef]
  4. Polturak, G.; Heinig, U.; Grossman, N.; Battat, M.; Leshkowitz, D.; Malitsky, S.; Rogachev, I.; Aharoni, A. Transcriptome and Metabolic Profiling Provides Insights into Betalain Biosynthesis and Evolution in Mirabilis Jalapa. Mol. Plant 2018, 11, 189–204. [Google Scholar] [CrossRef]
  5. Chiou, C.-Y.; Pan, H.-A.; Chuang, Y.-N.; Yeh, K.-W. Differential Expression of Carotenoid-Related Genes Determines Diversified Carotenoid Coloration in Floral Tissues of Oncidium Cultivars. Planta 2010, 232, 937–948. [Google Scholar] [CrossRef]
  6. Winkel-Shirley, B. Flavonoid Biosynthesis. A Colorful Model for Genetics, Biochemistry, Cell Biology, and Biotechnology. Plant Physiol. 2001, 126, 485–493. [Google Scholar] [CrossRef]
  7. Holton, T.A.; Brugliera, F.; Tanaka, Y. Cloning and Expression of Flavonol Synthase from Petunia hybrida. Plant J. 1993, 4, 1003–1010. [Google Scholar] [CrossRef]
  8. Saito, K.; Yonekura-Sakakibara, K.; Nakabayashi, R.; Higashi, Y.; Yamazaki, M.; Tohge, T.; Fernie, A.R. The Flavonoid Biosynthetic Pathway in Arabidopsis: Structural and Genetic Diversity. Plant Physiol. Biochem. 2013, 72, 21–34. [Google Scholar] [CrossRef]
  9. Tohge, T.; de Souza, L.P.; Fernie, A.R. Current Understanding of the Pathways of Flavonoid Biosynthesis in Model and Crop Plants. J. Exp. Bot. 2017, 68, 4013–4028. [Google Scholar] [CrossRef]
  10. Kitts, D.; Tomiuk, S. Studies on Mitigating Lipid Oxidation Reactions in a Value-Added Dairy Product Using a Standardized Cranberry Extract. Agriculture 2013, 3, 236–252. [Google Scholar] [CrossRef]
  11. Peng, X.Q.; Ai, Y.J.; Pu, Y.T.; Wang, X.J.; Li, Y.H.; Wang, Z.; Zhuang, W.B.; Yu, B.J.; Zhu, Z.Q. Transcriptome and Metabolome Analyses Reveal Molecular Mechanisms of Anthocyanin-Related Leaf Color Variation in Poplar (Populus deltoides) Cultivars. Front. Plant Sci. 2023, 14, 1103468. [Google Scholar] [CrossRef]
  12. Li, S.; Deng, B.; Tian, S.; Guo, M.; Liu, H.; Zhao, X. Metabolic and Transcriptomic Analyses Reveal Different Metabolite Biosynthesis Profiles between Leaf Buds and Mature Leaves in Ziziphus jujuba Mill. Food Chem. 2021, 347, 129005. [Google Scholar] [CrossRef]
  13. Jiao, F.; Zhao, L.; Wu, X.; Song, Z.; Li, Y. Metabolome and Transcriptome Analyses of the Molecular Mechanisms of Flower Color Mutation in Tobacco. BMC Genom. 2020, 21, 611. [Google Scholar] [CrossRef]
  14. Lu, J.; Zhang, Q.; Lang, L.; Jiang, C.; Wang, X.; Sun, H. Integrated Metabolome and Transcriptome Analysis of the Anthocyanin Biosynthetic Pathway in Relation to Color Mutation in Miniature Roses. BMC Plant Biol. 2021, 21, 257. [Google Scholar] [CrossRef]
  15. Yu, M.; Man, Y.; Lei, R.; Lu, X.; Wang, Y. Metabolomics Study of Flavonoids and Anthocyanin-Related Gene Analysis in Kiwifruit (Actinidia chinensis) and Kiwiberry (Actinidia arguta). Plant Mol. Biol. Rep. 2020, 38, 353–369. [Google Scholar] [CrossRef]
  16. Jiang, L.; Yue, M.; Liu, Y.; Ye, Y.; Zhang, Y.; Lin, Y.; Wang, X.; Chen, Q.; Tang, H. Alterations of Phenylpropanoid Biosynthesis Lead to the Natural Formation of Pinkish-Skinned and White-Fleshed Strawberry (Fragaria × Ananassa). Int. J. Mol. Sci. 2022, 23, 7375. [Google Scholar] [CrossRef]
  17. Grotewold, E. The Genetics and Biochemistry of Floral Pigments. Annu. Rev. Plant Biol. 2006, 57, 761–780. [Google Scholar] [CrossRef]
  18. Tanaka, Y.; Brugliera, F.; Chandler, S. Recent Progress of Flower Colour Modification by Biotechnology. Int. J. Mol. Sci. 2009, 10, 5350–5369. [Google Scholar] [CrossRef]
  19. Dasgupta, K.; Thilmony, R.; Stover, E.; Oliveira, M.L.; Thomson, J. Novel R2R3-MYB Transcription Factors from Prunus Americana Regulate Differential Patterns of Anthocyanin Accumulation in Tobacco and Citrus. GM Crops Food 2017, 8, 85–105. [Google Scholar] [CrossRef]
  20. Allan, A.C.; Hellens, R.P.; Laing, W.A. MYB Transcription Factors That Colour Our Fruit. Trends Plant Sci. 2008, 13, 99–102. [Google Scholar] [CrossRef]
  21. Ramsay, N.A.; Glover, B.J. MYB–BHLH–WD40 Protein Complex and the Evolution of Cellular Diversity. Trends Plant Sci. 2005, 10, 63–70. [Google Scholar] [CrossRef] [PubMed]
  22. Butelli, E.; Titta, L.; Giorgio, M.; Mock, H.-P.; Matros, A.; Peterek, S.; Schijlen, E.G.W.M.; Hall, R.D.; Bovy, A.G.; Luo, J.; et al. Enrichment of Tomato Fruit with Health-Promoting Anthocyanins by Expression of Select Transcription Factors. Nat. Biotechnol. 2008, 26, 1301–1308. [Google Scholar] [CrossRef]
  23. Feng, C.; Ding, D.; Feng, C.; Kang, M. The Identification of an R2R3-MYB Transcription Factor Involved in Regulating Anthocyanin Biosynthesis in Primulina Swinglei Flowers. Gene 2020, 752, 144788. [Google Scholar] [CrossRef]
  24. Xie, Y.; Tan, H.; Ma, Z.; Huang, J. DELLA Proteins Promote Anthocyanin Biosynthesis via Sequestering MYBL2 and JAZ Suppressors of the MYB/BHLH/WD40 Complex in Arabidopsis thaliana. Mol. Plant 2016, 9, 711–721. [Google Scholar] [CrossRef]
  25. Donoso, A.; Rivas, C.; Zamorano, A.; Peña, Á.; Handford, M.; Aros, D. Understanding Alstroemeria Pallida Flower Colour: Links between Phenotype, Anthocyanins and Gene Expression. Plants 2020, 10, 55. [Google Scholar] [CrossRef]
  26. Fu, M.; Yang, X.; Zheng, J.; Wang, L.; Yang, X.; Tu, Y.; Ye, J.; Zhang, W.; Liao, Y.; Cheng, S.; et al. Unraveling the Regulatory Mechanism of Color Diversity in Camellia Japonica Petals by Integrative Transcriptome and Metabolome Analysis. Front. Plant Sci. 2021, 12, 685136. [Google Scholar] [CrossRef]
  27. Rabiner, L.R. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. IEEE 1989, 77, 257–286. [Google Scholar] [CrossRef]
  28. Pollier, J.; Vanden Bossche, R.; Rischer, H.; Goossens, A. Selection and Validation of Reference Genes for Transcript Normalization in Gene Expression Studies in Catharanthus roseus. Plant Physiol. Biochem. 2014, 83, 20–25. [Google Scholar] [CrossRef]
  29. Khoo, H.E.; Azlan, A.; Tang, S.T.; Lim, S.M. Anthocyanidins and Anthocyanins: Colored Pigments as Food, Pharmaceutical Ingredients, and the Potential Health Benefits. Food Nutr. Res. 2017, 61, 1361779. [Google Scholar] [CrossRef] [PubMed]
  30. Yao, X.; Yao, Y.; An, L.; Li, X.; Bai, Y.; Cui, Y.; Wu, K. Accumulation and Regulation of Anthocyanins in White and Purple Tibetan Hulless Barley (Hordeum vulgare L. Var. Nudum Hook. f.) Revealed by Combined de Novo Transcriptomics and Metabolomics. BMC Plant Biol. 2022, 22, 391. [Google Scholar] [CrossRef] [PubMed]
  31. Shi, C.; Liu, L.; Wei, Z.; Liu, J.; Li, M.; Yan, Z.; Gao, D. Anthocyanin Accumulation and Molecular Analysis of Correlated Genes by Metabolomics and Transcriptomics in Sister Line Apple Cultivars. Life 2022, 12, 1246. [Google Scholar] [CrossRef] [PubMed]
  32. Wang, F.; Ji, G.; Xu, Z.; Feng, B.; Zhou, Q.; Fan, X.; Wang, T. Metabolomics and Transcriptomics Provide Insights into Anthocyanin Biosynthesis in the Developing Grains of Purple Wheat (Triticum aestivum L.). J. Agric. Food Chem. 2021, 69, 11171–11184. [Google Scholar] [CrossRef]
  33. Coen, E.S.; Meyerowitz, E.M. The War of the Whorls: Genetic Interactions Controlling Flower Development. Nature 1991, 353, 31–37. [Google Scholar] [CrossRef]
  34. Ausin, I.; Alonso-Blanco, C.; Martinez-Zapater, J.-M. Environmental Regulation of Flowering. Int. J. Dev. Biol. 2005, 49, 689–705. [Google Scholar] [CrossRef]
  35. Iwashina, T. Contribution to Flower Colors of Flavonoids Including Anthocyanins: A Review. Nat. Prod. Commun. 2015, 10, 529–544. [Google Scholar] [CrossRef]
  36. Qian, R.; Ye, Y.; Hu, Q.; Ma, X.; Zhang, X.; Zheng, J. Metabolomic and Transcriptomic Analyses Reveal New Insights into the Role of Metabolites and Genes in Modulating Flower Colour of Clematis Tientaiensis. Horticulturae 2023, 9, 14. [Google Scholar] [CrossRef]
  37. Zhong, H.; Liu, Z.; Zhang, F.; Zhou, X.; Sun, X.; Li, Y.; Liu, W.; Xiao, H.; Wang, N.; Lu, H.; et al. Metabolomic and Transcriptomic Analyses Reveal the Effects of Self- and Hetero-Grafting on Anthocyanin Biosynthesis in Grapevine. Hortic. Res. 2022, 9, uhac103. [Google Scholar] [CrossRef]
  38. Honda, C.; Kotoda, N.; Wada, M.; Kondo, S.; Kobayashi, S.; Soejima, J.; Zhang, Z.; Tsuda, T.; Moriguchi, T. Anthocyanin Biosynthetic Genes Are Coordinately Expressed during Red Coloration in Apple Skin. Plant Physiol. Biochem. 2002, 40, 955–962. [Google Scholar] [CrossRef]
  39. Ban, Y.; Kondo, S.; Ubi, B.E.; Honda, C.; Bessho, H.; Moriguchi, T. UDP-Sugar Biosynthetic Pathway: Contribution to Cyanidin 3-Galactoside Biosynthesis in Apple Skin. Planta 2009, 230, 871–881. [Google Scholar] [CrossRef]
  40. Aza-González, C.; Herrera-Isidrón, L.; Núñez-Palenius, H.G.; De La Vega, O.M.; Ochoa-Alejo, N. Anthocyanin Accumulation and Expression Analysis of Biosynthesis-Related Genes during Chili Pepper Fruit Development. Biol. Plant 2013, 57, 49–55. [Google Scholar] [CrossRef]
  41. Rahim, M.A.; Busatto, N.; Trainotti, L. Regulation of Anthocyanin Biosynthesis in Peach Fruits. Planta 2014, 240, 913–929. [Google Scholar] [CrossRef]
  42. Zhuang, H.; Lou, Q.; Liu, H.; Han, H.; Wang, Q.; Tang, Z.; Ma, Y.; Wang, H. Differential Regulation of Anthocyanins in Green and Purple Turnips Revealed by Combined De Novo Transcriptome and Metabolome Analysis. Int. J. Mol. Sci. 2019, 20, 4387. [Google Scholar] [CrossRef] [PubMed]
  43. Ying, H.; Shi, J.; Zhang, S.; Pingcuo, G.; Wang, S.; Zhao, F.; Cui, Y.; Zeng, X. Transcriptomic and Metabolomic Profiling Provide Novel Insights into Fruit Development and Flesh Coloration in Prunus Mira Koehne, a Special Wild Peach Species. BMC Plant Biol. 2019, 19, 463. [Google Scholar] [CrossRef]
  44. Luo, P.; Ning, G.; Wang, Z.; Shen, Y.; Jin, H.; Li, P.; Huang, S.; Zhao, J.; Bao, M. Disequilibrium of Flavonol Synthase and Dihydroflavonol-4-Reductase Expression Associated Tightly to White vs. Red Color Flower Formation in Plants. Front. Plant Sci. 2016, 6, 1257. [Google Scholar] [CrossRef]
  45. Han, Y.; Vimolmangkang, S.; Soria-Guerra, R.E.; Korban, S.S. Introduction of Apple ANR Genes into Tobacco Inhibits Expression of Both CHI and DFR Genes in Flowers, Leading to Loss of Anthocyanin. J. Exp. Bot. 2012, 63, 2437–2447. [Google Scholar] [CrossRef] [PubMed]
  46. Huang, H.; Xia, E.-H.; Zhang, H.-B.; Yao, Q.-Y.; Gao, L.-Z. De Novo Transcriptome Sequencing of Camellia Sasanqua and the Analysis of Major Candidate Genes Related to Floral Traits. Plant Physiol. Biochem. 2017, 120, 103–111. [Google Scholar] [CrossRef]
  47. Li, Z.; Vickrey, T.L.; McNally, M.G.; Sato, S.J.; Clemente, T.E.; Mower, J.P. Assessing Anthocyanin Biosynthesis in Solanaceae as a Model Pathway for Secondary Metabolism. Genes 2019, 10, 559. [Google Scholar] [CrossRef]
  48. Boase, M.R.; Lewis, D.H.; Davies, K.M.; Marshall, G.B.; Patel, D.; Schwinn, K.E.; Deroles, S.C. Isolation and Antisense Suppression of Flavonoid 3′,5′-Hydroxylase Modifies Flower Pigments and Colour in Cyclamen. BMC Plant Biol. 2010, 10, 107. [Google Scholar] [CrossRef]
Agronomy 13 02290 g001
Figure 1. Color phenotypes and anthocyanin contents of three cultivar. (a) Phenotypes of C. roseus petals. (b) Anthocyanin content (fresh weight) of CW, CP, and CR samples. **: p < 0.01.
Figure 1. Color phenotypes and anthocyanin contents of three cultivar. (a) Phenotypes of C. roseus petals. (b) Anthocyanin content (fresh weight) of CW, CP, and CR samples. **: p < 0.01.
Agronomy 13 02290 g001
Agronomy 13 02290 g002
Figure 2. Analysis of the differentially accumulated flavonoids. (a) Principal component analysis (PCA) of DAFs. (b) The heatmap analysis of all DAFs’ relative content. A, anthocyanidins; B, chalcone; C, flavanone; D, flavone; E, flavonol; F, Isoflavone; G, Isoflavonol. Numbers on the right correspond to Table 1 compounds. (c) Venn diagram of all DAFs in CW, CR, and CP. (d) Comparison of the DAFs profiles among CW, CR, and CP. (e) Venn diagram of overlapping DAFs in CR vs. CW, CP vs. CW, and CP vs. CR.
Figure 2. Analysis of the differentially accumulated flavonoids. (a) Principal component analysis (PCA) of DAFs. (b) The heatmap analysis of all DAFs’ relative content. A, anthocyanidins; B, chalcone; C, flavanone; D, flavone; E, flavonol; F, Isoflavone; G, Isoflavonol. Numbers on the right correspond to Table 1 compounds. (c) Venn diagram of all DAFs in CW, CR, and CP. (d) Comparison of the DAFs profiles among CW, CR, and CP. (e) Venn diagram of overlapping DAFs in CR vs. CW, CP vs. CW, and CP vs. CR.
Agronomy 13 02290 g002
Agronomy 13 02290 g003
Figure 3. Analysis of the differentially accumulated anthocyanin. (a) The heatmap analysis of all anthocyanin relative content. (b) Statistics of overlapping anthocyanin content.
Figure 3. Analysis of the differentially accumulated anthocyanin. (a) The heatmap analysis of all anthocyanin relative content. (b) Statistics of overlapping anthocyanin content.
Agronomy 13 02290 g003
Agronomy 13 02290 g004
Figure 4. Analysis of DEGs. (a) Correlation of all samples. A correlation analysis between the samples was performed to estimate biological duplication among samples within a group. (b) Volcano plot of DEGs between CR and CW. (c) Volcano plot of DEGs between CP and CW. (d) Number of DEGs identified in CW vs. CR, CW vs. CP, and CR vs. CP. (e) Venn diagram of overlapping DEGs between the CW vs. CP and CW vs. CP.
Figure 4. Analysis of DEGs. (a) Correlation of all samples. A correlation analysis between the samples was performed to estimate biological duplication among samples within a group. (b) Volcano plot of DEGs between CR and CW. (c) Volcano plot of DEGs between CP and CW. (d) Number of DEGs identified in CW vs. CR, CW vs. CP, and CR vs. CP. (e) Venn diagram of overlapping DEGs between the CW vs. CP and CW vs. CP.
Agronomy 13 02290 g004
Agronomy 13 02290 g005
Figure 5. Results of top 25 KEGG enriched pathways by DEGs in CR vs. CW (a) and CP vs. CW (b). Rich factor represents the degree of enrichment of genes under the designated pathway term. The greater the value of the rich factor, the greater the DEGs of pathway enrichment is. The red frames indicated the phenylpropanoid and flavonoid biosynthesis pathways.
Figure 5. Results of top 25 KEGG enriched pathways by DEGs in CR vs. CW (a) and CP vs. CW (b). Rich factor represents the degree of enrichment of genes under the designated pathway term. The greater the value of the rich factor, the greater the DEGs of pathway enrichment is. The red frames indicated the phenylpropanoid and flavonoid biosynthesis pathways.
Agronomy 13 02290 g005
Agronomy 13 02290 g006
Figure 6. The identification of all DEGs in anthocyanin biosynthesis pathway. The colored boxes represent the different branches of anthocyanin synthesis. The expression of related genes in the RNA-seq data (TMM) is represented by the rectangle. The high/low expression levels are represented by red/blue, respectively. Abbreviations: PAL, phenylalanine ammonia-lyase; 4CL, 4-coumarate--CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; F3′5′H, flavonoid 3′,5′-hydroxylase; DFR, dihydroflavonol 4-reductase; ANS, anthocyanidin synthase; BZ1, anthocyanidin 3-O-glucosyltransferase; UGT75C1, anthocyanidin 3-O-glucoside 5-O-glucosyltransferase; MT, methyltransferase; FLS, flavonol synthase.
Figure 6. The identification of all DEGs in anthocyanin biosynthesis pathway. The colored boxes represent the different branches of anthocyanin synthesis. The expression of related genes in the RNA-seq data (TMM) is represented by the rectangle. The high/low expression levels are represented by red/blue, respectively. Abbreviations: PAL, phenylalanine ammonia-lyase; 4CL, 4-coumarate--CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; F3′5′H, flavonoid 3′,5′-hydroxylase; DFR, dihydroflavonol 4-reductase; ANS, anthocyanidin synthase; BZ1, anthocyanidin 3-O-glucosyltransferase; UGT75C1, anthocyanidin 3-O-glucoside 5-O-glucosyltransferase; MT, methyltransferase; FLS, flavonol synthase.
Agronomy 13 02290 g006
Agronomy 13 02290 g007
Figure 7. Transcription factors (TFs) differentially expressed in petals of different colors based on the RNA-seq data. (a) TFs with a differential expression pattern in CR vs. CW. (b) TFs with a differential expression pattern in CP vs. CW. (c) TFs with a differential expression pattern in CR vs. CP. (d) Venn diagram of up-regulated MYBs in CP vs. CW and CR vs. CW. (e) Venn diagram of up-regulated bHLHs in CP vs. CW and CR vs. CW. (f) Venn diagram of up-regulated WD40s in CP vs. CW and CR vs. CW.
Figure 7. Transcription factors (TFs) differentially expressed in petals of different colors based on the RNA-seq data. (a) TFs with a differential expression pattern in CR vs. CW. (b) TFs with a differential expression pattern in CP vs. CW. (c) TFs with a differential expression pattern in CR vs. CP. (d) Venn diagram of up-regulated MYBs in CP vs. CW and CR vs. CW. (e) Venn diagram of up-regulated bHLHs in CP vs. CW and CR vs. CW. (f) Venn diagram of up-regulated WD40s in CP vs. CW and CR vs. CW.
Agronomy 13 02290 g007
Agronomy 13 02290 g008
Figure 8. Correlation heatmap analysis. (a) Correlation between anthocyanin contents and structural genes. (b) Correlation between structural genes and transcription factors (TFs). *: p < 0.05, **: p < 0.01, ***: p < 0.001.
Figure 8. Correlation heatmap analysis. (a) Correlation between anthocyanin contents and structural genes. (b) Correlation between structural genes and transcription factors (TFs). *: p < 0.05, **: p < 0.01, ***: p < 0.001.
Agronomy 13 02290 g008
Table 1. The identification results of DAFs.
Table 1. The identification results of DAFs.
NameClassReference Ionm/zRT [min]Attribution
1 Cyanidin 3-O-glucoside Anthocyanins [M + H] + 1 449.11 9.78 CW, CP
2 Cyanidin 3-O-rutinoside Anthocyanins [M + H] + 1 595.17 9.06 CW, CP, CR
3 Delphinidin 3,5-diglucoside Anthocyanins [M + H] + 1 627.16 7.89 CW, CP, CR
4 Delphinidin 3-O-glucoside Anthocyanins [M + H] + 1 465.10 9.66 CW, CP, CR
5 Delphinidin 3-O-glc-1-3-rham-1-6-glucoside Anthocyanins [M + H] + 1 773.21 8.52 CW, CP, CR
6 Delphinidin 3-O-rehamnoside Anthocyanins [M + H] + 1 449.11 8.78 CW, CP, CR
7 Malvidin 3-O-glucoside Anthocyanins [M + H] + 1 493.13 8.73 CP
8 Peonidin 3-O-glucoside Anthocyanins [M + H] + 1 463.12 8.65 CW, CR
9 Petunidin 3-O-glucoside Anthocyanins [M + H] + 1 479.12 8.20 CP, CR
10 Petunidin-rutinoside Anthocyanins [M + H] + 1 625.18 10.74 CW, CP, CR
11 Licochalcone B Chalcones [M + H] + 1 287.09 9.10 CW, CR
12 3,5,7,3′,4′,5′-Hexahydroxy-6,8-dimethylflavanone Flavanone [M − H] − 1 347.08 9.55 CP, CR
13 4′-Methoxy-7-O-beta-D-glucopyranosyl-8,3′-dihydroxyflavanone Flavanone [M + H] + 1 465.14 8.76 CW, CR
14 diosmin Flavanone [M + H] + 1 609.18 8.47 CW, CP, CR
15 Eriodictyol Flavanone [M + H] + 1 289.07 9.11 CW, CP, CR
16 Hesperetin Flavanone [M + H] + 1 303.09 7.35 CR
17 Isosakuranin Flavanone [M + H] + 1 449.14 9.73 CW
18 Isoscutellarein 7-(6′″-acetylallosyl-(1->2)-glucoside) Flavanone [M − H] − 1 651.16 10.34 CW, CR
19 Sakuranetin Flavanone [M + H] + 1 287.09 9.73 CW, CP, CR
20 Eriodictyol 7,3′-dimethyl ether Flavanone [M + H] + 1 317.10 10.65 CW, CR
21 5,7,3′,6′-Tetrahydroxy-8,2′-dimethoxyflavone 6′-glucoside Flavone [M − H] − 1 507.11 10.25 CP, CR
22 Luteolin Flavone [M − H] − 1 285.04 12.95 CW, CP, CR
23 Pectolinarin Flavone [M + H] + 1 623.20 8.96 CW, CP, CR
24 Rhamnetin Flavone [M + H] + 1 317.07 10.74 CW, CP, CR
25 Tricin 5-O-glucoside Flavone [M + H] + 1 493.13 10.84 CW, CR
26 cirsimarin Flavone [M + H] + 1 477.14 9.19 CW, CR
27 Isorhamnetin Flavonol [M + H] + 1 317.07 9.83 CW, CP, CR
28 Kaempferol Flavonol [M + H] + 1 287.06 9.06 CW, CP, CR
29 Mauritianin Flavonol [M + H] + 1 741.22 9.06 CW, CP, CR
30 Kaempferol 3-galactoside Flavonol [M + H] + 1 449.11 9.98 CW, CP
31 kaempferol 3-glucoside Flavonol [M − H] − 1 447.09 10.11 CW, CP, CR
32 kaempferol 3-rhamnoside Flavonol [M + H] + 1 433.11 9.66 CW, CP
33 Quercetin Flavonol [M + H] + 1 303.05 9.36 CW, CP, CR
34 Quercetin 3-galactoside Flavonol [M + H] + 1 465.10 9.37 CW, CP, CR
35 Quercetin 3-O-glc-1-3-rham-1-6-glucoside Flavonol [M − H] − 1 463.09 9.67 CR
36 Quercetin-3-O-glucoside Flavonol [M + H] + 1 773.21 7.81 CW, CP, CR
37 Rutin Flavonol [M + H] + 1 611.16 8.78 CW, CP, CR
38 Iridin Isoflavone [M − H] − 1 521.13 9.99 CW, CP, CR
39 Isokaempferide Isoflavonol [M + H] + 1 301.07 11.06 CW, CP, CR
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Xiao, Y.; Tang, Y.; Huang, X.; Zeng, L.; Liao, Z. Integrated Transcriptomics and Metabolomics Analysis Reveal Anthocyanin Biosynthesis for Petal Color Formation in Catharanthus roseus. Agronomy 2023, 13, 2290. https://doi.org/10.3390/agronomy13092290

AMA Style

Xiao Y, Tang Y, Huang X, Zeng L, Liao Z. Integrated Transcriptomics and Metabolomics Analysis Reveal Anthocyanin Biosynthesis for Petal Color Formation in Catharanthus roseus. Agronomy. 2023; 13(9):2290. https://doi.org/10.3390/agronomy13092290

Chicago/Turabian Style

Xiao, Yuchen, Yueli Tang, Xianhui Huang, Lingjiang Zeng, and Zhihua Liao. 2023. "Integrated Transcriptomics and Metabolomics Analysis Reveal Anthocyanin Biosynthesis for Petal Color Formation in Catharanthus roseus" Agronomy 13, no. 9: 2290. https://doi.org/10.3390/agronomy13092290

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop