Next Article in Journal
Experimental and Theoretical Studies on Acid Corrosion Inhibition of API 5L X70 Steel with Novel 1-N-α-d-Glucopyranosyl-1H-1,2,3-Triazole Xanthines
Previous Article in Journal
Heat Capacities of L-Cysteine, L-Serine, L-Threonine, L-Lysine, and L-Methionine
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Molecules
Volume 28
Issue 1
10.3390/molecules28010462
molecules-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

Analysis of Floral Color Differences between Different Ecological Conditions of Clematis tangutica (Maxim.) Korsh

by
Xiaozhu Guo
1,2,
Gui Wang
1,2,
Juan Li
1,2,
Jiang Li
1,2 and
Xuemei Sun
1,2,*
1
Academy of Agriculture & Forestry, Qinghai University, Xining 810016, China
2
Laboratory for Research and Utilization of Germplasm Resources in Qinghai Tibet Plateau, Xining 810016, China
*
Author to whom correspondence should be addressed.
Molecules 2023, 28(1), 462; https://doi.org/10.3390/molecules28010462
Submission received: 15 November 2022 / Revised: 6 December 2022 / Accepted: 20 December 2022 / Published: 3 January 2023
Browse Figures
Review Reports Versions Notes

Abstract

:
The Clematis tangutica (Maxim.) Korsh. is a wild flowering plant that is most widely distributed on the Qinghai–Tibet Plateau, with beautiful, brightly colored flowers and good ornamental properties and adaptability. In diverse natural environments, the blossom color of C. tangutica (Maxim.) Korsh. varies greatly, although it is unclear what causes this diversity. It was examined using UPLC-MS/MS and transcriptome sequencing for the investigation of various compounds, differentially expressed genes (DEGs), and flavonoid biosynthesis-related pathways in two flowers in two ecological settings. The results showed that a total of 992 metabolites were detected, of which 425 were differential metabolites, mainly flavonoid metabolites associated with its floral color. The most abundant flavonoids, flavonols and anthocyanin metabolites in the G type were cynaroside, isoquercitrin and peonidin-3-O-glucoside, respectively. Flavonoids that differed in multiplicity in G type and N type were rhoifolin, naringin, delphinidin-3-O-rutinoside, chrysoeriol and catechin. Rhoifolin and chrysoeriol, produced in flavone and flavonol biosynthesis, two flavonoid compounds of C. tangutica (Maxim.) Korsh. with the largest difference in floral composition in two ecological environments. In two ecological environments of flower color components, combined transcriptome and metabolome analyses revealed that BZ1-1 and FG3-1 are key genes for delphinidin-3-O-rutinoside in anthocyanin biosynthesis, and HCT-5 and FG3-3 are key genes for rhoifolin and naringin in flavonoid biosynthesis and flavone and flavonol. Key genes for chlorogenic acid in flavonoid biosynthesis include HCT-6, CHS-1 and IF7MAT-1. In summary, differences in flavonoids and their content are the main factors responsible for the differences in the floral color composition of C. tangutica (Maxim.) Korsh. in the two ecological environments, and are associated with differential expression of genes related to flavonoid synthesis.

1. Introduction

Flower color, a vital floricultural trait in ornamental plants, is the result of the accumulation of flavonoids, carotenoids and betalains [1]. Flavonoids are a kind of secondary metabolites that are widely distributed in plants and are among the most important pigments, the different composition and content of which can lead to different colors in plants [2,3]. Meanwhile, flavonoids are one of the most important pigments in ornamental petals and fruits, such as Chrysanthemum morifolium [4], Dahlia variabilis [5], Rosa hybrida L. [6], rubus fruits [7] and Nymphaea ”Blue Bird” [8]. However, the composition of flavonoids may vary considerably between the differently colored petals of the same species. Study has shown that the detection and analysis of the flower color components of pink and white chrysanthemums reveal that the two anthocyanins in pink chrysanthemums are the main flavonoids, while the flower color components in white chrysanthemums are flavonoids rather than anthocyanins [9]. Analysis of pigments in purple, red, orange, yellow and white Lycoris longituba petals showed that only cyanidin-3-xylosylglucoside (Cy3XyGlc) anthocyanins were present in all purple, red and orange petals, while no anthocyanins were detected in the white and yellow petals [10]. Because among flavonoids, anthocyanin belongs to the red series and controls the pink to blue-violet flower color, the other flavonoids belong to the pure yellow series, among which chalcones and aurones are deep yellow, and flavones, flavonols and flavanones are light yellow or nearly colorless [11]. As flavonoids, anthocyanin have highly characteristic C6-C3-C6 carbon skeletons and similar biosynthetic sources [12]. Due to the instability of anthocyanin, most of them exist in glycosylated form [13]. Currently, there are several hundred anthocyanin [14]; the six most common anthocyanin are: pelargonidin(Pg) [15], cyanidin(Cy) [16], delphinidin(Dp) [17], peonidin(Pn) [18], petunidin(Pt) [19] and malvidin(Mv) [20]. Among them, Pg, Cy and Dp are the three main anthocyanin [21]. Studies have shown that Pn is derived from Cy and Pt and Mv from Dp [21].
The pathway of flavonoid biosynthesis in plants is clear [22,23,24]. The starting material of this pathway was phenylalanine, which was catalyzed by phenylalanine ammonia-lyase (PAL), cinnamic acid 4-hydroxylase (C4H) and 4 coumarate CoA ligase (4CL) to produce p-coumaroyl-CoA. Subsequently, p-coumaroyl-CoA was catalyzed by chalcone synthase (CHS), and chalcone isomerase (CHI) to generate chalcones and flavanones in turn, forming the basic structure of anthocyanin. Flavanones were then catalyzed by flavanone 3-hydroxylase(F3H) to produce dihydroflavonols, a branch of the pathway of anthocyanin biosynthesis. Dihydroflavonols can be catalyzed to form dihydroquercetin, dihyromyricetin flavonoids, kaempferol, pelargonidin and flavonols, which are subsequently catalyzed by dihydroflavonol reductase (DFR) to form colored and unstable floral glycosides such as pelargonidin, cyanidin and delphinidin. Finally, the three anthocyanin are catalyzed by anthocyanidin synthase (ANS), glycosyltransferases (UGTs) and methyltransferases (MT) to form stable anthocyanin [25], which are transported to the vesicles for storage [26].
C. florida Thunb is one of the world’s most important horticultural plants, with a wide variety of colors and great ornamental value, and is known as the “Queen of Vines” [27]. C. tangutica (Maxim.) Korsh. [28] is a widely distributed plant on the Qinghai–Tibet Plateau, growing in grasslands, thickets and rock piles in alpine areas at altitudes of over 3000 m. It has excellent ecological adaptations such as drought tolerance, cold tolerance and light tolerance, and its beautiful, brightly colored flowers and long flowering period can be used as a vertical greening material. It has high ornamental value. At present, most of the research on it is focused on its medicinal value.
Qinghai has a large altitudinal span, with a wide variation in habitat at different altitudes, and the flowers of Ganqing clematis growing there differ greatly in morphology and color (Figure 1). However, differences in floral color components and their key synthesis genes have not been reported for C. tangutica (Maxim.) Korsh., and it is not clear how floral color differences relate to the different ecological environments in which they occur. Therefore, the clarification of the floral pigment composition and the key genes for its biosynthesis in it are of great importance in exploring the adaptation of floral color to adversity as well as in breeding and production. In this study, the flowers of wild C. tangutica (Maxim.) Korsh. from two ecological environments were used for qualitative and quantitative analysis of flower metabolites using UPLC-MS/MS to investigate the relationship between flower color and anthocyanin metabolites. Identification of different anthocyanin metabolites and analysis of associated differentially expressed genes (DEGs) were based on metabolome and transcriptome. The study’s findings not only offer potential candidates for the metabolic network of anthocyanin biosynthesis in the flowers, but they also offer useful references for further functional validation of the potential candidates and enrichment of the database of anthocyanin components in Ranunculaceae Juss.

2. Results

2.1. Analysis of Metabolites in the Flowers of C. tangutica (Maxim.) Korsh

The metabolites were studied in order to clarify the differences in flower color between the two ecological conditions of the C. tangutica (Maxim.) Korsh. A total of 992 metabolites were identified in its flowers, of which 177 were flavonoid metabolites (Table S1). The flavonoid metabolites are grouped into nine categories, including 63 flavonols, 38 flavones, 19 anthocyanin, 19 flavanonols, 13 flavonoid carbonoside, 11 isoflavones, seven flavanones, five chalcones and two flavanols. Of the 992 metabolites, there were 425 differential metabolites. Apart from phenolic acids, flavonoids have the highest number of differential metabolites, with 100 species. The results show that the skeletons of most of the flavonoids in it include apigenin, quercetin, kaempferol and lignan. Most of the flavonoids are O-glycosides and only a few are C-glycosides. Among the metabolites of flavonoids, quercetin, kaempferol, apigenin, lignan, delphinidin, cyanidin, geranophyllin, genistein and paeoniflorin are abundant in it. Among the differential metabolites, three were more than five-fold higher in the G type than in the N type, namely catechin (31.44-flod), pinobanksin (21.40-flod) and peonidin-3-O-glucoside (5.36-flod). In addition, the levels of chlorogenic acid and 5-O-caffeoylshikimic acid generated in flavonoid biosynthesis were significantly higher in the G type than in the N type.
A total of 19 anthocyanin were identified in C. tangutica (Maxim.) Korsh., including delphinidin, cyanidin, peonidin, petunidin and rosinidin. Of these, the levels of peonidin-3-O-glucoside, delphinidin-3-O-glucoside (myrtillin) and delphinidin-3,5,3′-Tri-O-glucoside in the G type were significantly higher than those in the N type (Figure 2). Thus, several compounds, chlorogenic acid, catechin, pinobanksin and peonidin-3-O-glucoside, may play an important role in its flower color.
Metabolome correlation analysis of the samples was carried out to assess the biological replication between samples within the group. The Pearson’s correlation coefficient r (PCR) was used as an indicator for the assessment of biological repeat relevance. The closer the absolute value of R is to 1, the stronger the correlation between the two replicate samples. The results showed that there were large differences between sample groups and small differences within groups (Figure 3).
The overlap display analysis of the total ion flow plots analyzed by mass spectrometric detection of different QC samples showed high overlap of the curves of the total ion flow for metabolite detection, i.e., the retention time and peak intensity were consistent, indicating good signal stability of the mass spectrometry for the same sample at different times (Figure 4A,B; Table S2). The high stability of the instrument provides an important guarantee of repeatability and reliability of the data, where N stands for the negative ion mode and P for the positive ion mode.
Overall, the flavonoids in the N type were higher than the flavonoids in the G type flowers, suggesting that these differential metabolites associated with flower color may be the main reason for the differences in flower color between the two types.

2.2. Differential Genes in Clematis tangutica (Maxim.) Korsh. Flowers in Different Ecological Environments

In order to study the gene expression of the pigment constituents of the flowers of C. tangutica (Maxim.) Korsh. in different ecological environments, we sequenced the transcriptome of the G type and N type. The six transcriptome samples produced a total of 41.4 Gb clean data, with each sample reaching 6 Gb or more of clean data and 92% or more of Q30 bases. Comparison and annotation of the clean read yielded 123,331 genes, of which 81,893 were newly identified. There were 32,036 DEGs between the two types.
To analyze the function of DEGs in two types, KEGG enrichment analysis was performed on their DEGs and differential metabolites, demonstrating the extent of the enrichment of pathways with differential genes at the same time. The KEGG enrichment p-value results showed that the most DEGs were enriched in metabolic pathways (ko01100), followed by biosynthesis of secondary metabolites (ko01110), and the rest in order of plant hormone signal transduction (ko04075), phenylpropanoid biosynthesis (ko00940) and carotenoid biosynthesis (ko00906) (Figure 5). There are many DEGs associated with flavonoid synthesis between its two different floral colors, further confirming that the flavonoid and flavonol synthetic pathways and the anthocyanin synthetic pathway are the main metabolic pathways responsible for differences in floral color in different ecologies.

2.3. Expression Patterns of Genes Expressed in Relation to the Flavonoid Synthesis Pathway

To further investigate the differential metabolic network of the G type and N type substances, this study targeted 44 genes related to the flavonoid, anthocyanin, flavonoid and flavonol biosynthetic pathways for gene expression pattern analysis (Figure 6).
In the flavonoid pathway, cinnamoyl-CoA is used as a precursor substance, in turn, in the presence of trans-cinnamate 4-monooxygenase (CYP73A), chalcone synthase (CHS), chalcone isomerase (CHI), naringenin 3-dioxygenase (F3H) and bifunctional dihydroflavonol 4-reductase/flavanone 4-reductase (DFR) to produce pelargonidin, cyanidin and delphinidin into anthocyanin biosynthesis; in the presence of flavonoid 3’,5’-hydroxylase (CYP75A), CHS, CHI and flavonol synthase (FLS), apigenin and kaempferol are produced to enter flavone and flavonol biosynthesis. In the flavone and flavonol biosynthesis, apigenin and kaempferol are the initial precursors in CYP75A, isoflavone 7-O-glucoside-6″-O-malonyltransferase (IF7MAT), flavonol-3-O-glucoside/galactoside glucosyltransferase (FG3) and flavonol-3-O-glucoside L-rhamnosyltransferase (FG2) in the presence of a series of enzymes such as kaempferol 3-sophorotrioside, nictoflorin, chrysoeriol and rhoifolin.
Through a combined metabolome and transcriptome sequencing analysis, we screened 39 genes in the flavonoid biosynthetic pathway in the G type and N type, which had widely divergent expression patterns. The expression of genes in flavonoid biosynthesis, anthocyanin biosynthesis and flavone and flavonol biosynthesis differed between the two ecological environments, with some genes being highly expressed in the G type and lowly expressed in the N type; for example, CHS-1, CHS-5, CHI-1, CHI-6, HCT-2, etc. in flavonoid biosynthesis, and IF7MAT-1, IF7MAT-2, FG3-1, etc. in flavone and flavonol biosynthesis. Thus, high expression of genes such as CHS, CHI, HCT and IF7MAT upstream of flavonoid biosynthesis and flavone and flavonol biosynthesis may directly affect the synthesis of downstream metabolites.

2.4. Correlation between Flavonoid Content and Flavonoid Synthesis Gene Expression

To further investigate the key genes in the biosynthetic pathway of the floral components of C. tangutica (Maxim.) Korsh., we performed a correlation heat map analysis and correlation network analysis on the expression of genes related to the synthesis of anthocyanin and flavonoids in its floral color, leading to the preliminary identification of key genes in the anthocyanin and flavonoid synthetic pathways. The heat map of gene and metabolite correlations showed some variation in the correlation between genes in the anthocyanin and flavonoid synthesis pathways and anthocyanin and flavonoids, with individual genes in flavonoid biosynthesis, anthocyanin biosynthesis and flavone and flavonol biosynthesis having varying degrees of influence on the synthesis of various flavonoids (Figure 7).
The G type and N type have significantly different anthocyanin compositions and their synthesis-related genes are mainly CHS-1, BZ1-1, BZ1-2, BZ1-3, HCT-2, IF7MAT-1, FLS and other genes. In addition, chlorogenic acid generated in flavonoid biosynthesis was the phenolic acid compound with the largest fold difference between G type and N type pigment components and was most strongly correlated with the CHS-1 gene. Peonidin-3-O-glucoside, generated in the anthocyanin biosynthesis pathway, is the most abundant anthocyanin in its floral pigment composition and is most associated with the BZ1-3 gene. Isoquercitrin and cynaroside, produced in the flavone and flavonol biosynthesis pathway, are the two compounds with the highest relative flavonoid and flavonol content of G type pigment components. Rhoifolin and chrysoeriol are the two flavonoid compounds with the greatest fold difference between the G type and N type floral components, with rhoifolin and chrysoeriol best correlated with the FG3-3 and FG3-1 genes, respectively.
At the same time, we performed a correlation analysis between the flavonoid biosynthesis, anthocyanin biosynthesis and flavone and flavonol biosynthesis for the generation of significantly different delphinidin-3-O-rutinoside, rhoifolin chrysoeriol, naringin, chlorogenic acid and other major flavonoid and anthocyanin differential metabolites for correlation analysis. The results showed that delphinidin-3-O-rutinoside was positively correlated with the expression of FG3-1 and BZ1-1, suggesting that these genes play a role in the promotion of anthocyanin biosynthesis. Rhoifolin was positively correlated with the expression of HCT-2, HCT-4, HCT-5, FG3-3 and IF7MAT-2, and naringin was positively correlated with the expression of FG3-3 and HCT-5, suggesting that these genes play a positive regulatory role in flavonoid biosynthesis and flavone and flavonol biosynthesis. Chlorogenic acid was positively correlated with the expression of CHS-1, HCT-6 and IF7MAT-1, suggesting that these genes play a positive regulatory role in flavonoid biosynthesis (Figure 8).

3. Discussion

The appearance and color of a plant’s flowers are important indicators of its ornamental value, and all ornamental plants have their own ornamental characteristics [29]. Plant flowers show color differences, generally due to the important role played by metabolites such as anthocyanin, flavonoids or carotenoids, such as cyanidin 3,5-O-diglucoside, malvidin 3,5-diglucoside and cyanidin 3-O-galactoside in purple Salvia miltiorrhiza Bge. Flowers [30]; cyanidin 3-O-[2-O-(xylosyl)-galactoside] in Nerium oleander L. [31], etc. Flavonoids are among the most abundant secondary metabolites in the composition of plant anthocyanins and make an important contribution to plants. Other than that, flavonoids have some resistance to UV rays [32]. We sequenced the metabolome and transcriptome of C. tangutica (Maxim.) Korsh. flowers from different ecological environments to clarify the substances that differentiate its floral color and to identify key genes in the flavonoid biosynthesis pathway. The relative content of flavonoid compounds in the N type was higher than that in the G type. Chlorogenic acid, pinobanksin, peonidin-3-O-glucoside and myrtillin are the main substances in the floral pigment composition of the G type. Of these, rhoifolin, naringin, chrysoeriol and chlorogenic acid are the flavonoids with the greatest relative differences in the floral pigment composition of two types (chlorogenic acid [33] is a phenolic acid, but it binds to anthocyanin and has a stabilizing effect on them). In the same yellow flower, the concentration of flavonoids in lisianthus (Eustoma grandiflorum), Lathyrus chrysanthus and Dianthus caryophyllus petals is high [34]. The main pigment component in American lotus (Nelumbo lutea) is flavonols: quercetin 3-O-glucuronide [35]. Flavonoids are the main pigment component in paeonia with yellow flowers [36].
Correlation analysis of the flavonoids, related gene expressions and intergroup correlation network analysis of the floral components of C. tangutica (Maxim.) Korsh. showed that BZ1-1 and PG3-1 are key genes for delphinidin-3-O-rutinoside in anthocyanin biosynthesis, HCT-5 and FG3-3 are rhoifolin and naringin key genes in flavonoid biosynthesis and flavone and flavonol biosynthesis. HCT-6, CHS-1 and IF7MAT-1 are key genes for chlorogenic acid in flavonoid biosynthesis. In Acer truncatum [37], studies on its different leaf colors revealed that the key genes for the biosynthesis of phenyl propane are CHS, PAL, C4H and 4CL, the key genes for the biosynthesis of flavonoids are CHI, FLS and ANR, and the key genes for the biosynthesis of anthocyanin are ANS, DFR, HCT, BZ1 and GT1. A study of the molecular mechanism of petal discoloration in Nelumbo nucifera [38] revealed that the key genes for anthocyanin biosynthesis in its petals are CHS, F3H, ANS and UFGT. Fifteen genes related to flavonoids were identified in Hibiseu manihot L. [39], including CHS, CYP73A, CHI, F3H, DFR, HCT, ANR and others. It is suggested that differential genes such as CHS, HCT, PG3 and BZ1 in its petals may be responsible for the differences in flavonoid composition and their relative content, resulting in differences in the flower color of C. tangutica (Maxim.) Korsh. under different growth conditions.
The color of a plant’s flowers not only aids in insect pollination, but also serves as a mechanism for the plant to react to environmental changes. Plants’ bloom colors additionally have significant commercial importance. For example, they can be used as a raw material for the extraction of food additive colors [40], in cosmetics [41], medical treatment [42], as ornamental plants [43], greenery [44] and so on. The ecological conditions under which the two experimental materials used in the study were grown differed considerably in terms of altitude, temperature, humidity, light intensity, soil type, etc. Combined multiomics analyses are often used to investigate phenotypic and biological process regulation mechanisms in biological models. In this study, the relative content of flavonoids and the genes related to flavonoid synthesis in the floral color components of the C. tangutica (Maxim.) Korsh. were analyzed by metabolome and transcriptome sequencing techniques under different growth environments to clarify the differences in its floral color components and to lay the foundation for the study of the molecular mechanism of biosynthesis of different floral color substances as well as the influence of different ecological environments on its floral color as well as the selection and breeding of ornamental C. tangutica (Maxim.) Korsh. varieties. However, the causes of the environmental conditions responsible for the differences in flower color between the G and N type need further verification.

4. Materials and Methods

4.1. Plant Material

Flowers of the wildflower species C. tangutica (Maxim.) Korsh. from different ecological environments in Qinghai Province were used as test material (Figure 1) and were harvested from three biological replicates in Hainan Tibetan Autonomous Prefecture, Qinghai Province, G109, Gonghe County (36°70′ N, 99°54′ E; altitude 3507 m) and Xining City, Qinghai Province (36°72′ N, 101°7′ E; altitude 2270 m) (denoted by G type and N type for the flowers of the Gonghe and Xining regions, respectively). After being harvested, they were quickly frozen in liquid nitrogen and then stored in a −80 °C cold storage.

4.2. Extraction and Analysis of Flavonoid Metabolites

The samples were removed from the −80 °C freezer and placed in a freeze dryer (Scientz-100 F) for vacuum freeze drying. A grinding machine (MM 400, Retsch, Haan, Germany) (30 Hz, 1.5 min) ground them into powder. A 100 mg of powder was weighed and dissolved in 1.2 mL of 70% methanol extract. It was vortexed every 30 min, each session lasting 30 s, for a total of six vortexes. After centrifugation (12,000 rpm, 10 min), the supernatant was aspirated, and the sample was filtered through a microporous membrane (0.22 μm pore size) and stored in the injection vial for UPLC-MS/MS analysis.

4.3. RNA Extraction and Sequencing

RNA extraction, cDNA library construction and sequencing were all done by Metware (http://www.metware.cn/ accessed on 12 January 2021). The total RNA was extracted from flower samples using the RNAprep Pure Plant kit (TIANGEN, Beijing, China). The constructed cDNA libraries were sequenced using the Illumina HiSeq platform. After sequencing was completed, clean reads were obtained by fastp_0.19.3 [45] removing adapter related, containing N and Low quality. C. tangutica (Maxim.) Korsh. is a reference-free genome, and clean reads were spliced using Trinity [46] to obtain reference sequences for subsequent analysis.

4.4. Transcriptomic Data Analysis

Unigene sequences were compared to the KEGG, NR, Swiss-Prot, GO, KOG, Trembl databases using DIAMOND_v0.9.24.125 [47] BLAST software, and to the Pfam database using HNNER software to obtain annotation information for Unigene. Using bowtie_2 2.3.4.1 [48] of the RSEM_v1.3.1 [49] software, we obtained the transcriptome by splicing Trinity_v2.11.0 as the reference sequence, and the clean reads of the samples were mapped towards the reference sequence. To allow the number of fragments to truly reflect the level of transcript expression, the number of mapped reads and the length of transcripts at the sample site were normalized and FPKM (fragments per kilobase of transcript per million fragments mapped) was used as a measure of the level of transcript or gene expression.
Differential expression analysis between sample groups was performed by DESeq2_1.22.2 [50,51] using unstandardized gene reads count data to obtain differentially expressed gene sets between the two biological conditions. The reads count of genes was implemented using feature counts. The Benmini–Hochberg method was then used to apply multiple hypothesis correction to the probability of hypothesis testing (p-value) to obtain the false discovery rate (FDR). The differential genes were screened for |log2Fold Change| ≥ 1, and FDR < 0.05. The obtained DEGs were annotated to the KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology) and KOG (Cluster of Eukaryotic Orthologous Groups) databases and enrichment analysis was performed by applying hypergeometric tests.

4.5. Correlation Analysis and Correlation Network Analysis

In order to identify the genes that play a key role in the flavonoid biosynthesis pathway in the different flower colors of glycine clematis flowers, correlation analysis and correlation network analysis were used to establish the flavonoid content and expression levels of genes related to flavonoid synthesis in glycine clematis flowers from both regions. Correlation analysis was performed by R (base package) 3.5.1 (default parameters), where the correlation coefficient > 0.80 and p value < 0.05. Correlation network analysis was performed using Spearman of Cytoscape_v3.6.1 to calculate correlations, where the correlation coefficient > 0.95 and the p-value < 0.01.

4.6. Statistical Analysis

The experimental data were analyzed using SPSS_18.0 with normal distribution validation followed by independent sample t-tests to determine the significance of differences (p < 0.05). Hierarchical clustering analysis of metabolites between each sample was carried out using R 3.5.1. After normalization of the data, the heat map was plotted using heatmap (R)_1.0.12.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules28010462/s1, Table S1: Different flavonoid metabolites in G vs. N; Table S2: Total ion flow data for mixed sample mass spectra.

Author Contributions

X.G.: Formal analysis, investigation and writing—original draft preparation; G.W.: Formal analysis and validation; J.L. (Juan Li): Data curation and validation; J.L. (Jiang Li): Formal analysis; X.S.: Conceptualization and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Identification, innovation and gene mining of distinctive germplasm resources on the Qinghai–Tibetan plateau, item number: 2022-ZJ-902.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets are publicly available at NCBI with Sequence Read Archive (SRA) accession: PRJNA893815.

Conflicts of Interest

All the authors declare that they have no conflict of interest.

References

  1. Sun, W.; Zhou, N.; Wang, Y.; Sun, S.; Zhang, Y.; Ju, Z.; Yi, Y. Characterization and functional analysis of RdDFR1 regulation on flower color formation in Rhododendron delavayi. Plant Physiol. Biochem. 2021, 169, 203–210. [Google Scholar] [CrossRef] [PubMed]
  2. Panche, A.N.; Diwan, A.D.; Chandra, S.R. Flavonoids: An overview. J. Nutr. Sci. 2016, 5, e47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Halevas, E.G.; Avgoulas, D.I.; Katsipis, G.; Pantazaki, A.A. Flavonoid-liposomes formulations: Physico-chemical characteristics, biological activities and therapeutic applications. Eur. J. Med. Chem. Rep. 2022, 5, 100059. [Google Scholar] [CrossRef]
  4. Chen, S.; Liu, J.; Dong, G.; Zhang, X.; Liu, Y.; Sun, W.; Liu, A. Flavonoids and caffeoylquinic acids in Chrysanthemum morifolium Ramat flowers: A potentially rich source of bioactive compounds. Food Chem. 2021, 344, 128733. [Google Scholar] [CrossRef] [PubMed]
  5. Schlangen, K.; Miosic, S.; Halbwirth, H. Allelic variants from Dahlia variabilis encode flavonoid 3′-hydroxylases with functional differences in chalcone 3-hydroxylase activity. Arch. Biochem. Biophys. 2010, 494, 40–45. [Google Scholar] [CrossRef]
  6. Schmitzer, V.; Veberic, R.; Stampar, F. Prohexadione-Ca application modifies flavonoid composition and color characteristics of rose (Rosa hybrida L.) flowers. Sci. Hortic. 2012, 146, 14–20. [Google Scholar] [CrossRef]
  7. Wu, Y.; Zhang, C.; Huang, Z.; Lyu, L.; Li, J.; Li, W.; Wu, W. The color difference of rubus fruits is closely related to the composition of flavonoids including anthocyanins. LWT 2021, 149, 111825. [Google Scholar] [CrossRef]
  8. Zhao, Y.; Zhou, W.; Chen, Y.; Li, Z.; Song, X.; Wang, J.; Tian, D.; Niu, J. Metabolite analysis in Nymphaea ‘Blue Bird’ petals reveal the roles of flavonoids in color formation, stress amelioration, and bee orientation. Plant Sci. 2021, 312, 111025. [Google Scholar] [CrossRef]
  9. Chen, S.-M.; Li, C.-H.; Zhu, X.-R.; Deng, Y.-M.; Sun, W.; Wang, L.-S.; Chen, F.-D.; Zhang, Z. The identification of flavonoids and the expression of genes of anthocyanin biosynthesis in the chrysanthemum flowers. Biol. Plant. 2012, 56, 458–464. [Google Scholar] [CrossRef]
  10. He, Q.; Shen, Y.; Wang, M.; Huang, M.; Yang, R.; Zhu, S.; Wang, L.; Xu, Y.; Wu, R. Natural variation in petal color in Lycoris longituba revealed by anthocyanin components. PLoS ONE 2011, 6, e22098. [Google Scholar] [CrossRef]
  11. Zhao, D.; Tao, J. Recent advances on the development and regulation of flower color in ornamental plants. Front. Plant Sci. 2015, 261. [Google Scholar] [CrossRef] [Green Version]
  12. Zhang, Y.; Butelli, E.; Martin, C. Engineering anthocyanin biosynthesis in plants. Curr. Opin. Plant Biol. 2014, 19, 81–90. [Google Scholar] [CrossRef]
  13. Wallace, T.C.; Giusti, M.M. Anthocyanins. Adv. Nutr. 2015, 6, 620–622. [Google Scholar] [CrossRef] [Green Version]
  14. Verweij, W.; Spelt, C.; Di Sansebastiano, G.-P.; Vermeer, J.; Reale, L.; Ferranti, F.; Koes, R.; Quattrocchio, F. An H+ P-ATPase on the tonoplast determines vacuolar pH and flower colour. Nat. Cell Biol. 2008, 10, 1456–1462. [Google Scholar] [CrossRef]
  15. Yari, A.; Rashnoo, S. Optimization of a new method for extraction of cyanidin chloride and pelargonidin chloride anthocyanins with magnetic solid phase extraction and determination in fruit samples by HPLC with central composite design. J. Chromatogr. B 2017, 1067, 38–44. [Google Scholar] [CrossRef]
  16. Yang, L.; Rong-Rong, C.; Ji-Li, F.; Ke, Y. Total anthocyanins and cyanidin-3-O-glucoside contents and antioxidant activities of purified extracts from eight different pigmented plants. Pharmacogn. Mag. 2019, 15, 124. [Google Scholar]
  17. Kato, M.; Tani, T.; Terahara, N.; Tsuda, T. PLoS ONE 2015, 10, e0126157. [CrossRef]
  18. Toki, K.; Kawanishi, H. Survey of peonidin glycosides in garden snapdragons and changes in anthocyanin contents during floral development. J. Jpn. Soc. Hortic. Sci. 1992, 60, 989–995. [Google Scholar] [CrossRef]
  19. Catalano, G.; Fossen, T.; Andersen, Ø.M. Petunidin 3-O-α-rhamnopyranoside-5-O-β-glucopyranoside and other anthocyanins from flowers of Vicia villosa. J. Agric. Food Chem. 1998, 46, 4568–4570. [Google Scholar] [CrossRef]
  20. Peixoto, F.; Senna Gouvêa, A.; de Araújo Santiago, M.; de Sá Velosos Martins, Z.; Galhardo Borguini, R.; de Oliveira Godoy, R. Characterization and bioaccessibility of anthocyanins from blueberry (Vaccinium corymbosum L.) after simulated gastro-intestinal digestion: A positive effect on malvidin derivatives. Fruits 2018, 73, 101–109. [Google Scholar] [CrossRef]
  21. 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] [PubMed]
  22. Koes, R.E.; Quattrocchio, F.; Mol, J.N. The flavonoid biosynthetic pathway in plants: Function and evolution. BioEssays 1994, 16, 123–132. [Google Scholar] [CrossRef]
  23. Quattrocchio, F.; Baudry, A.; Lepiniec, L.; Grotewold, E. The regulation of flavonoid biosynthesis. In The Science of Flavonoids; Springer: Berlin/Heidelberg, Germany, 2006; pp. 97–122. [Google Scholar]
  24. Wan, L.; Lei, Y.; Yan, L.; Liu, Y.; Pandey, M.K.; Wan, X.; Varshney, R.K.; Fang, J.; Liao, B. Transcriptome and metabolome reveal redirection of flavonoids in a white testa peanut mutant. BMC Plant Biol. 2020, 20, 161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Forkmann, G. Flavonoids as flower pigments: The formation of the natural spectrum and its extension by genetic engineering. Plant Breed. 1991, 106, 1–26. [Google Scholar] [CrossRef]
  26. Isaak, C.K.; Petkau, J.C.; Blewett, H.; Karmin, O.; Siow, Y.L. Lingonberry anthocyanins protect cardiac cells from oxidative-stress-induced apoptosis. Can. J. Physiol. Pharmacol. 2017, 95, 904–910. [Google Scholar] [CrossRef] [Green Version]
  27. Jung, J.; Shin, M.; Jeong, N.; Hwang, D. Antioxidant and anti-inflammatory activities of ethanol extract of Clematis trichotoma Nakai. Korean J. Clin. Lab. Sci. 2021, 53, 165–173. [Google Scholar] [CrossRef]
  28. Wei, Y.; Chen, T.; Wang, S.; Shen, C.; Yan, S.; Song, Z.; Liu, Y.; Yang, F.; Hai, P.; Li, Y. Separation of a new triterpenoid saponin together with six known ones from Clematis tangutica (Maxim.) Korsh and evaluation of their cytotoxic activities. Nat. Prod. Res. 2021, 1–8. [Google Scholar] [CrossRef]
  29. Colquhoun, T.A.; Clark, D.G. Unraveling the regulation of floral fragrance biosynthesis. Plant Signal. Behav. 2011, 6, 378–381. [Google Scholar] [CrossRef] [Green Version]
  30. Jiang, T.; Zhang, M.; Wen, C.; Xie, X.; Tian, W.; Wen, S.; Lu, R.; Liu, L. Integrated metabolomic and transcriptomic analysis of the anthocyanin regulatory networks in Salvia miltiorrhiza Bge. flowers. BMC Plant Biol. 2020, 20, 349. [Google Scholar] [CrossRef]
  31. Tatsuzawa, F.; Yoshikoshi, A.; Takehara, A.; Suzuki, S. Flavonoids from the flowers of Adenium obesum (Forssk.) Roem. & Schult. Mandevilla sanderi (Hemsl.) Woodson, and Nerium oleander L. (Apocynaceae). Biochem. Syst. Ecol. 2021, 99, 104347. [Google Scholar]
  32. Jiang, L.; Yanase, E.; Mori, T.; Kurata, K.; Toyama, M.; Tsuchiya, A.; Yamauchi, K.; Mitsunaga, T.; Iwahashi, H.; Takahashi, J. Relationship between flavonoid structure and reactive oxygen species generation upon ultraviolet and X-ray irradiation. J. Photochem. Photobiol. A Chem. 2019, 384, 112044. [Google Scholar] [CrossRef]
  33. Gras, C.C.; Bogner, H.; Carle, R.; Schweiggert, R.M. Effect of genuine non-anthocyanin phenolics and chlorogenic acid on color and stability of black carrot (Daucus carota ssp. sativus var. atrorubens Alef.) anthocyanins. Food Res. Int. 2016, 85, 291–300. [Google Scholar] [CrossRef]
  34. Markham, K.R.; Gould, K.S.; Ryan, K.G. Cytoplasmic accumulation of flavonoids in flower petals and its relevance to yellow flower colouration. Phytochemistry 2001, 58, 403–413. [Google Scholar] [CrossRef]
  35. Liu, Q.; Zhang, D.; Liu, F.; Liu, Z.; Wang, X.; Yang, Y.; Li, S.; Li, H.; Tian, D.; Wang, L. Quercetin-derivatives paint the yellow petals of American lotus (Nelumbo lutea) and enzymatic basis for their accumulation. Hortic. Plant J. 2022; in press. [Google Scholar] [CrossRef]
  36. Yang, Y.; Li, B.; Feng, C.; Wu, Q.; Wang, Q.; Li, S.; Yu, X.; Wang, L. Chemical mechanism of flower color microvariation in Paeonia with yellow flowers. Hortic. Plant J. 2020, 6, 179–190. [Google Scholar] [CrossRef]
  37. Qiao, Q.; Si, F.; Wu, C.; Wang, J.; Zhang, A.; Tao, J.; Zhang, L.; Liu, Y.; Feng, Z. Transcriptome sequencing and flavonoid metabolism analysis in the leaves of three different cultivars of Acer truncatum. Plant Physiol. Biochem. 2022, 171, 1–13. [Google Scholar] [CrossRef]
  38. Liu, J.; Wang, Y.; Zhang, M.; Wang, Y.; Deng, X.; Sun, H.; Yang, D.; Xu, L.; Song, H.; Yang, M. Color fading in lotus (Nelumbo nucifera) petals is manipulated both by anthocyanin biosynthesis reduction and active degradation. Plant Physiol. Biochem. 2022, 179, 100–107. [Google Scholar] [CrossRef]
  39. Zhou, Y.; Xu, X.; Chen, Y.; Gao, J.; Shi, Q.; Tian, L.; Cao, L. Combined Metabolome and Transcriptome Analyses Reveal the Flavonoids Changes and Biosynthesis Mechanisms in Different Organs of Hibiseu manihot L. Front. Plant Sci. 2022, 13, 817378. [Google Scholar] [CrossRef]
  40. Roriz, C.L.; Heleno, S.A.; Carocho, M.; Rodrigues, P.; Pinela, J.; Dias, M.I.; Fernandes, I.P.; Barreiro, M.F.; Morales, P.; Barros, L.; et al. Betacyanins from Gomphrena globosa L. flowers: Incorporation in cookies as natural colouring agents. Food Chem. 2020, 329, 127178. [Google Scholar] [CrossRef]
  41. López-Hortas, L.; Falqué, E.; Domínguez, H.; Torres, M.D. Microwave hydrodiffusion and gravity versus conventional distillation for Acacia dealbata flowers. Recovery of bioactive extracts for cosmetic purposes. J. Clean. Prod. 2020, 274, 123143. [Google Scholar] [CrossRef]
  42. Manzoor, M.; Singh, J.; Gani, A.; Noor, N. Valorization of natural colors as health-promoting bioactive compounds: Phytochemical profile, extraction techniques, and pharmacological perspectives. Food Chem. 2021, 362, 130141. [Google Scholar] [CrossRef] [PubMed]
  43. Huang, B.; Rong, H.; Ye, Y.; Ni, Z.; Xu, M.; Zhang, W.; Xu, L.-A. Transcriptomic analysis of flower color variation in the ornamental crabapple (Malus spp.) half-sib family through Illumina and PacBio Sequel sequencing. Plant Physiol. Biochem. 2020, 149, 27–35. [Google Scholar] [CrossRef] [PubMed]
  44. Tomitaka, M.; Uchihara, S.; Goto, A.; Sasaki, T. Species richness and flower color diversity determine aesthetic preferences of natural-park and urban-park visitors for plant communities. Environ. Sustain. Indic. 2021, 11, 100130. [Google Scholar] [CrossRef]
  45. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef] [PubMed]
  46. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; Adiconis, X.; Fan, L.; Raychowdhury, R.; Zeng, Q. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011, 29, 644–652. [Google Scholar] [CrossRef] [Green Version]
  47. Buchfink, B.; Xie, C.; Huson, D.H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 2015, 12, 59–60. [Google Scholar] [CrossRef]
  48. Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [Green Version]
  49. Li, B.; Dewey, C.N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011, 12, 323. [Google Scholar] [CrossRef] [Green Version]
  50. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [Green Version]
  51. Varet, H.; Brillet-Guéguen, L.; Coppée, J.-Y.; Dillies, M.-A. SARTools: A DESeq2-and EdgeR-based R pipeline for comprehensive differential analysis of RNA-Seq data. PLoS ONE 2016, 11, e0157022. [Google Scholar] [CrossRef]
Molecules 28 00462 g001 550
Figure 1. The flower of Clematis tangutica (Maxim.) Korsh. (A) G type (B) N type.
Figure 1. The flower of Clematis tangutica (Maxim.) Korsh. (A) G type (B) N type.
Molecules 28 00462 g001
Molecules 28 00462 g002 550
Figure 2. Heat map of the relative content of flavonoids in G type and N type (different colors represent Z-score normalized values for the relative content of metabolites; red represents high compound content, green represents low compound content).
Figure 2. Heat map of the relative content of flavonoids in G type and N type (different colors represent Z-score normalized values for the relative content of metabolites; red represents high compound content, green represents low compound content).
Molecules 28 00462 g002
Molecules 28 00462 g003 550
Figure 3. Correlation plot between G type and N type samples.
Figure 3. Correlation plot between G type and N type samples.
Molecules 28 00462 g003
Molecules 28 00462 g004 550
Figure 4. Total ion flow diagram for mixed sample mass spectrometry ((A) QC-MS-TIC-N (B) QC-MS-TIC-P).
Figure 4. Total ion flow diagram for mixed sample mass spectrometry ((A) QC-MS-TIC-N (B) QC-MS-TIC-P).
Molecules 28 00462 g004
Molecules 28 00462 g005 550
Figure 5. Scatter plot of differential gene KEGG enrichment in G type and N type.
Figure 5. Scatter plot of differential gene KEGG enrichment in G type and N type.
Molecules 28 00462 g005
Molecules 28 00462 g006 550
Figure 6. Expression patterns of flavonoid biosynthesis-related genes in flowers of G type and N type.
Figure 6. Expression patterns of flavonoid biosynthesis-related genes in flowers of G type and N type.
Molecules 28 00462 g006
Molecules 28 00462 g007 550
Figure 7. Correlation analysis of flavonoids and flavonoid synthesis genes in flowers of Clematis tangutica (Maxim.) Korsh.
Figure 7. Correlation analysis of flavonoids and flavonoid synthesis genes in flowers of Clematis tangutica (Maxim.) Korsh.
Molecules 28 00462 g007
Molecules 28 00462 g008 550
Figure 8. Metabolites and genes combined analysis network (pink represents genes; green represents metabolites. Solid lines represent positive correlations between metabolites and genes; dashed lines represent negative correlations between metabolites and genes. Shorter lines represent stronger correlations between metabolites and genes).
Figure 8. Metabolites and genes combined analysis network (pink represents genes; green represents metabolites. Solid lines represent positive correlations between metabolites and genes; dashed lines represent negative correlations between metabolites and genes. Shorter lines represent stronger correlations between metabolites and genes).
Molecules 28 00462 g008
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

Guo, X.; Wang, G.; Li, J.; Li, J.; Sun, X. Analysis of Floral Color Differences between Different Ecological Conditions of Clematis tangutica (Maxim.) Korsh. Molecules 2023, 28, 462. https://doi.org/10.3390/molecules28010462

AMA Style

Guo X, Wang G, Li J, Li J, Sun X. Analysis of Floral Color Differences between Different Ecological Conditions of Clematis tangutica (Maxim.) Korsh. Molecules. 2023; 28(1):462. https://doi.org/10.3390/molecules28010462

Chicago/Turabian Style

Guo, Xiaozhu, Gui Wang, Juan Li, Jiang Li, and Xuemei Sun. 2023. "Analysis of Floral Color Differences between Different Ecological Conditions of Clematis tangutica (Maxim.) Korsh" Molecules 28, no. 1: 462. https://doi.org/10.3390/molecules28010462

APA Style

Guo, X., Wang, G., Li, J., Li, J., & Sun, X. (2023). Analysis of Floral Color Differences between Different Ecological Conditions of Clematis tangutica (Maxim.) Korsh. Molecules, 28(1), 462. https://doi.org/10.3390/molecules28010462

Article Metrics

Back to TopTop