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Comprehensive analysis of metabolome and transcriptome reveals the mechanism of color formation in different leave of Loropetalum Chinense var. Rubrum



Loropetalum chinense var. rubrum (L. chinense var. rubrum) is a precious, coloured-leaf native ornamental plant in the Hunan Province. We found an L. chinense var. rubrum tree with three different leaf colours: GL (green leaf), ML (mosaic leaf), and PL (purple leaf). The mechanism of leaf coloration in this plant is still unclear. Therefore, this study aimed to identify the metabolites and genes involved in determining the colour composition of L. chinense var. rubrum leaves, using phenotypic/anatomic observations, pigment content detection, and comparative metabolomics and transcriptomics.


We observed that the mesophyll cells in PL were purple, while those in GL were green and those in ML were a mix of purple-green. The contents of chlorophyll a, b, carotenoids, and total chlorophyll in PL and ML were significantly lower than those in GL. While the anthocyanin content in PL and ML was significantly higher than that in GL. The metabolomics results showed the differences in the content of cyanidin 3-O-glucoside, delphinidin 3-O-glucoside, cyanidin 3,5-O-diglucoside, pelargonidin, and petunidin 3,5-diglucoside in ML, GL, and PL were significant. Considering that the change trend of anthocyanin content change was consistent with the leaf colour difference, we speculated that these compounds might influence the colour of L. chinense var. rubrum leaves. Using transcriptomics, we finally identified nine differentially expressed structural genes (one ANR (ANR1217); four CYP75As (CYP75A1815, CYP75A2846, CYP75A2909, and CYP75A1716); four UFGTs (UFGT1876, UFGT1649, UFGT1839, and UFGT3273) and nine transcription factors (two MYBs (MYB1057 and MYB1211), one MADS-box (MADS1235), two AP2-likes (AP2-like1779 and AP2-like2234), one bZIP (bZIP3720), two WD40s (WD2173 and WD1867) and one bHLH (bHLH1631) that might be related to flavonoid biosynthesis and then impacted the appearance of colour in L. chinense var. rubrum leaves.


This study revealed potential molecular mechanisms associated with leaf coloration in L. chinense var. rubrum by analyzing differential metabolites and genes related to the anthocyanin biosynthesis pathway. It also provided a reference for research on leaf colour variation in other ornamental plants.


Loropetalum chinense var. rubrum belongs to the Hamamelidaceae (witch-hazel family) [1] and is mainly distributed in the belt south of the middle Yangtze River to the north of the Tropic of Cancer in China. It originated in the Hunan Province and played an important role in the landscape [2]. L. chinense var. rubrum is an evergreen plant with an elegant tree shape and brightly coloured foliage. Its leaves, flowers, and roots were used in traditional Chinese medicine for treating cough, burns, abdominal pain, etc. [3]. Recently, the plant has gained a lot of interest and has been widely cultivated for its ornamental and medicinal value.

As an ornamental plant, the colour of L. chinense var. rubrum leaves are one of its most significant characteristics. Leaf coloration is determined by the pigment in the mesophyll cells, such as chlorophyll, carotenoids, and flavonoids [4, 5]. Flavonoids are comprised of chalcones, flavone, flavonol, and anthocyanins, which colour plants blue, pink, yellow, purple, and red [6, 7]. Anthocyanins are considered to be the main coloration pigments, while flavone and flavonol are the synergistic pigments [8, 9]. Anthocyanins have been proved to determine the appearance of colour in many fruits, flowers, and vegetables, such as Vaccinium corymbosum [10], Morella rubra [11], Centaurea cyanus [12], Primula vulgaris [13], and Allium cepa L (Onion) [14]. In addition, they also played a vital role in the physiological activities of plants and human health. They have been shown to alleviate the stress of cold, drought, and pests on plants [15,16,17], and also contributed to protecting the human body from oxidative stress, cancer, bacterial infection, and cardiovascular and neurodegenerative diseases [18, 19].

Anthocyanins, which are the product of the phenylpropanoid pathway, are the central issue in the study of plant colour, which is [20], and its synthetic pathway has been well-characterized in Arabidopsis thaliana and Petunia [21, 22]. First, phenylalanine is required as a substrate to be converted to cinnamic acid in the presence of phenylalanine ammonia-lyase (PAL) [23]. Cinnamic acid is then converted into various dihydroflavonols by a series of enzymes, such as cinnamate 4-hydroxylase (C4H), 4-coumarate-CoA ligase (4CL), chalcone synthase (CHS), chalcone isomerase (CHI), clavanone3-hydroxylase (F3H), flavonoid 3’-hydroxylase (F3’H) and flavonoid 3’5’ -hydroxylase (F3’5’H) [20, 24]. Subsequently, dihydroflavonol4-reductase (DFR) catalyses the conversion of dihydroflavonols to leucoanthocyanidins [25], which are finally converted into anthocyanins by anthocyanidin synthase (ANS) [26]. Anthocyanins are the end products of the anthocyanin synthesis pathway and are divided into six groups: cyanidin, pelargonidin, delphinidin, peonidin, petunidin, and malvidin [27, 28]. However, they are unstable in the cytoplasm and require further glycosylation (GT), methylation (MT), and acylation (AT) to be stored in vacuoles [29] (Fig. 1). The anthocyanin derivatives produced vary among plant species. For example, anthocyanin 3-O-glucoside in Ipomoea nil was glycosylated to form anthocyanin 3-O-sophoroside [30]; Vitis vinifera was pigmented through glycosylation and methylation to generate procyanidin-3-glucoside and paeoniflorin-3-glucoside [31]; Chrysanthemum×morifolium was pigmented by acylating cyanidin 3-O-glucoside to cyanidin-3-O-(6’’-malonylglucoside) [32].

Fig. 1
figure 1

Biosynthesis pathway of anthocyanin. PAL (phenylalanine ammonia-lyase); C4H (cinnamate 4-monooxygenase); 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 (dihydroflflavonol 4-reductase); ANS (anthocyanidin synthase); GT (glucosyltransferases); MT (methyltransferases) and AT (acyltransferases)

The transcription factor families MYB, bHLH, and WD40 are also involved in anthocyanin synthesis by regulating the expression of structural genes [33]. MYB is one of the most abundant family of transcription factors in higher plants, which is related to regulating secondary metabolism, cell morphogenesis and differentiation, signal transduction, and stress response [34,35,36,37]. They regulate the expression of the early genes in anthocyanin synthesis [38], such as PAL, C4H, 4CL, CHS, and CHI [39,40,41]. The bHLH transcription factors regulate anthocyanin synthesis by binding to either MYB transcription factors or WD40 proteins, or both of them to form an MBW protein complex [42,43,44]. Therefore, a thorough investigation is warranted to find out the relationship between leaf colour and anthocyanin species.

In this study, the transcriptional and metabolic data on L. chinense var. rubrum leaves of three colours (green, mosaic and purple) were compared to identify the key metabolites and genes that regulate leaf colour formation, to clarify the molecular and metabolic mechanisms underlying the different pigmentations and to provide a basis for colour improvement in ornamental plants.


Leaf colour observation and pigment content determination

To understand the general colour characteristics of L. chinense var. rubrum leaves, leaves of three colours were observed quantitatively, anatomically, and microscopically. The colour of the leaves was consistent with that of the pigment cells or cell clusters inside. The three colours of leaves that were examined were: green leaves (GL), purple leaves (PL), and mosaic leaves (ML) (Fig. 2A1-C1). When observed at a magnification of 20×, the upper epidermal cells of ML leaves had a small amount of purple pigment, while those of PL leaves had a large amount of purple pigment, and those of GL leaves had a large amount of green pigment (Fig. 2A2-C2). Meanwhile, the transverse section was observed to show that the L. chinense var. rubrum leaves had typical structural characteristics such as upper epidermis, palisade tissue, sponge tissue, and lower epidermis (Fig. 2A3-C3). Chlorophyll and anthocyanin were mainly in the mesophyll cells of leaves. The mesophyll cells of ML leaves were purple and green, while those of PL leaves were mostly purple and those of GL leaves were green ( Fig. 2A3-C3).

Fig. 2
figure 2

Pictures show mesophyll cells and sections of ML, PL, and GL leaves in turn, scale bars = 100 μm. A1-C1 ML, PL, and GL leaves; A2-C2 Microscopic observation of leaf epithelial cell, scale bars = 100 μm; A3-C3 The anatomical structure of a transverse section of the blade, scale bars = 100 μm

To further evaluate the leaf colour objectively, we used the CIELAB system to detail various leaf colour indexes (L*, a*, b*) and detect pigment contents. The L* (lightness) parameter varies from 100 (white) to 0 (black), A positive value of a* indicates more red than green, and a positive value of b* means more yellow than blue [45]. Chromatic value analysis showed that the L* and b* of GL leaves were significantly higher than those of ML leaves, while the a* value of ML and PL leaves was higher compared with GL leaves (Table 1).

We quantified the photosynthetic pigment and total anthocyanin contents in the leaves of the three samples (Fig. 3) (Additional file 1). For photosynthetic pigment, we found notable differences among the three samples. GL had the highest content of photosynthetic pigment, around 2-3-fold higher than ML and PL, while the corresponding differences between ML and PL were insignificant (Fig. 3A, B, C, D). PL had the highest anthocyanin content (Fig. 3E).

Fig. 3
figure 3

The X-axis indicates the name of the sample, and the Y-axis indicates the absolute content of the extracted fresh weight. A Absolute content of chlorophyll (A) B Absolute content of chlorophyll (B) C Absolute content of carotenoid concentration. D Absolute contents of total chlorophyll. E Absolute contents of total anthocyanins. The data represent six biological repeats and their average. Mean (± SE) with different lower letters are significantly different within the (mean separation by LSD and Duncan’s test at P < 0.05)

Table 1 The leaf color difference values of ML, PL, and GL.


a-cuticle; b-epidermis from adaxial leaf surface; c-palisade issue; d-spongy issue.

Statistical analysis of metabolomic data

Physiological data showed that the anthocyanin contents in the different-coloured leaves of L. chinense var. rubrum differed significantly; however, the reason for this difference remains unclear. We profiled the metabolome of the three samples using the liquid chromatography-tandem mass spectrometry metabolomics approach. A total of 207 compounds were detected in the L. chinense var. rubrum leaf, which was grouped into eight classes: proanthocyanins, polyphenol, isoflavone, flavonol, flavanone, anthocyanins, flavonoids, and flavones (Additional file 2). The 207 metabolites were analyzed by principal component analysis to compare the metabolite compositions involved in the pigmentation of the leaves. The compositions of the three samples separated significantly in the first principal component (38.5% of the total variable) and the second principal component (26.2% of the total variable) (Fig. 4A), indicating that the ML, PL, and GL leaves had significant inter-group differences.

A total of 37, 35, and 41 differential metabolites were selected in GL vs. ML, GL vs. PL, and ML vs. PL, respectively (Additional file 3), with a total of 11 overlaps (Fig. 4B). The annotations of the different groups of metabolites in the various pathways of flavonoid biosynthesis (ko0094) and anthocyanin biosynthesis (ko00942) were shown in Additional file 4.

Fig. 4
figure 4

Differential metabolites from different leaves. A PCA score plot of three materials and numbers of potential markers for each leaf color. B Venn diagram shows the overlapping and cultivar-specific differential metabolites from ML, PL, and GL

Anthocyanin content in the three-coloured leaves

Interestingly, the leaf colours are closely related to the content of anthocyanin-related metabolites. Using metabolomics, we isolated and identified 15 anthocyanins from leaf extracts cyanidin 3-O-glucoside, peonidin, cyanidin O-syringic acid, delphinidin, delphinidin 3-O-glucoside, cyanidin 3-O-rutinoside, cyanidin 3,5-O-diglucoside, pelargonin, petunidin 3-O-glucoside, pelargonidin 3-O-beta-D-glucoside, cyanidin, cyanidin 3-O-galactoside, petunidin 3,5-diglucoside, malvidin 3-acetyl-5-diglucoside, and peonidin 3-sophoroside-5-glucoside. Notably, the results of total anthocyanin content were consistent with those of anthocyanin content determination, following the trend PL > ML > GL leaves (Fig. 2E). The same kind of anthocyanins was examined from the three-coloured leaves, but their levels differed significantly (Table 2). The leaves of different colours might differ in anthocyanin biosynthesis or the expression of regulatory genes.

Table 2 Type and content of anthocyanins in leaves of ML, PL, and GL.

Sample quality control (QC) analysis

Three standardized cDNA libraries were constructed from the RNA of GL, ML, and PL. After the cDNA library was cleaned and characterized, a total of 186,694,570,149,946,386 and 123,143,062 reads were obtained, respectively. The percentages of reads having Q20 (an error probability of 0.02%) were 98.09%, 97.48%, and 97.9% for GL, ML, and PL, respectively. The GC contents of the reads were approximately 43.73%, 43.98%, and 43.84%, respectively (Additional file 5). These clean reads were assembled into 231,810 unigenes ranging from 65 to 2135 bp in length (average 1271 bp) and an N50 of 2608 bp (Additional file 6). The sequencing quality covered the majority of expressed genes in GL, ML, and PL, providing a reference for further analysis. We compared the obtained sequences with the information in seven databases; 118,518 (17.88%) unigenes had homologues in the nr database, 96,572 (14.57%) in SwissProt, 115,058 (17.36%) in KEGG, 75,254 (11.36%) in KOG, 84,662 (12.77%) in GO, 88,001 (13.28%) in NT and 84,662 (12.77%) in Pfam databases, respectively (Additional file 7). We calculated the correlation coefficient of samples according to the fragments per kilobase of transcript per million mapped reads (FPKM) value to evaluate the reliability of the measured gene expression levels. The higher the similarity was, the closer the Pearson coefficient was to 1, indicating that the measurement was reliable (Fig. 5A).

Fig. 5
figure 5

Differential expression genes in different colors. A Thermal diagram of the correlation coefficient between leaves. The Pearson correlation coefficient is within [-1, 1], and the closer it is to 1 or -1, the stronger the positive/anti-linear relationship. B Venn diagram of DEGs

The intersection of differentially expressed genes (DEGs) in three-coloured leaves

The Venn diagram more intuitively showed the overlap of DEGs in the three comparison groups (Fig. 5B). There were 5646 DEGs (3447 downregulated, 2199 upregulated) between the GL vs. ML group, 4217 DEGs (3539 downregulated, 678 upregulated) between the GL vs. PL group and 6836 DEGs (2613 downregulated, 4223 upregulated) between the ML vs. PL group (Additional file 8).

The results of GO database annotation, presented in Additional file 9, showed that DEGs in the three-coloured can be successfully annotated into three biological processes.

The KEGG database is pathway-related. To further study the biochemical pathways of these DEGs, they were mapped onto the KEGG database [46]. Notably, KEGG pathway enrichment analysis in the pairwise comparisons of DEGs between two groups highlighted several metabolic processes including flavone and flavanol biosynthesis (ko00942) and flavonoid biosynthesis (ko00941), which were ​closely related to anthocyanin synthesis (Additional file 10). These pathways provided insights into the metabolic processes underlying different leaf pigmentations in L. chinense var. rubrum.

Genes involved in anthocyanin biosynthesis

To further study the determinants of colour diversity in ML, PL, and GL, the anthocyanin synthesis metabolic pathway was emphasized. Anthocyanins played an important role in plant coloration. Therefore, pathways related to anthocyanin synthesis were screened out from 241 DEGs (Fig. 4B), among these nine DEGs showed significant changes in expression levels: one ANR (ANR1217), four CYP75A (CYP75A1815, CYP75A2846, CYP75A2909, and CYP75A1716) and four UFGTs (UFGT1876, UFGT1649, UFGT1839, and UFGT3273) (Additional file 11). As the key gene in the biosynthesis of delphinidin, CYP75A catalysed the conversion of its major substrate, dihydrokaempferol, to dihydromyricetin [47]. ANR [48] and UFGT [49] convert the substrate of anthocyanin into (-)- epicatechin and anthocyanin, respectively. In this study, we found that these nine genes (particularly UFGTs) were expressed considerably higher in PL than in ML and GL (Additional file 12). However, little is known about the UFGT gene of L. chinense var. rubrum and its function needs to be further studied.

Genes encoding transcription factors

Transcription factors participate in the synthesis and accumulation of metabolites by modulating the expression levels of structural genes. Therefore, we screened genes related to the flavonoid synthesis pathway by comparing transcription factors between the three-coloured leaves. Genes involved in flavonoid biosynthesis are usually regulated by MYB, bHLH, WD40, bZIP, and MADS-box transcription factors [50]. Nine transcription factors were selected from these families (Additional file 12), including two MYBs (MYB1057 and MYB1211), one MADS-box (MADS1235), two AP2-likes (AP2-like1779 and AP2-like2234), one bZIP (bZIP3720), two WD40s (WD2173 and WD1867) and one bHLH (bHLH1631). In this study, we found that AP2-like2224, bZIP3720, WD1867, WD2173, and bHLH1631 were all upregulated in PL, while the MYBs, MADS-box, and AP2-like1779 were upregulated in ML and GL.

Validation of transcriptome results by quantitative reverse transcription PCR (qRT-PCR)

To verify the expression levels of structural genes and transcription factors related to anthocyanin synthesis in L. chinense var. rubrum, we selected nine structural genes and nine transcription factors for qRT-PCR analysis (Additional file 12), and their correlation was evaluated (Additional file 13). ANR and CYP75A had higher expression levels in GL and ML than PL (Fig. 6A). Although ANR promotes the formation of (-)- epicatechin, it weakens the transformation ability of anthocyanins and leads to the formation of green leaves. ​In contrast, UFGT was highly expressed in PL leaves but had lower or no expression in ML and GL, respectively. Thus, we speculated that the upregulated expression of the UFGT gene might contribute to anthocyanin synthesis and regulate the formation of mosaic and purple leaves.

Fig. 6
figure 6

FPKM values calculated from the transcriptomic data, and transcriptional levels of flavonoid biosynthetic genes in the L. chinense var. rubrum detected by qRT-PCR analysis. The β-actin gene was used as an internal control. AANR, UFGT, and CYP75A. MYB, MADS-box, AP2-like, bZIP, WD40 and bHLH. The control for relative expression GL was assigned the arbitrary value of 1.0. The data represent the six biological repeats and their average. Error bars represent the standard deviations of six biological replicates

We verified expression trends of candidate transcription factors, such as AP2-like2224, bZIP3720, WD1867, WD2173, and bHLH1631; they were consistent with the UFGT gene expression trend with higher expression in the PL compared with GL and ML (Fig. 6B). At the same time, the Pearson correlation coefficient showed a strong correlation (R2 > 0.92) between the five transcription factors and the UFGT gene (Additional file 14), suggesting that these transcription factors were related to anthocyanin biosynthesis and regulated the appearance of leaf colour.


In ornamental plants with colourful leaves, research on leaf colour has always been the focus since it affects the ornamental quality and commercial value of the plants. However, the mechanism of leaf coloration in L. chinense var. rubrum was still unclear, necessitating its study using the existing technique.

In this study, anatomic and microscopic observations, pigment content determination, flavonoid metabolomics, and transcriptome sequencing were performed on L. chinense var. rubrum leaves of three different colours. Anatomic observations under the microscope showed that the mesophyll cells in ML were a mix of purple and green, those in PL were all purple, and those in GL were all green. On this basis, the determination of L. chinense var. rubrum pigment content proved the existence of chlorophyll, carotenoids, and anthocyanins in plant leaves. We speculated that ML had the pigments chlorophyll and anthocyanin, PL had anthocyanin and GL had chlorophyll. A total of 207 flavonoid compounds were detected using metabolomics. DEGs related to pigmentation were found in the transcriptome of the three-coloured leaves. To screen the main components and candidate genes of pigmentation, we proposed a hypothetical biosynthetic pathway (Fig. 7).

Fig. 7
figure 7

Putative genes in the anthocyanin synthesis pathway and their expression level. Heatmaps were constructed based on log2 (FPKM) of leaves ML, PL, and GL

Our pigment content results showed that chlorophyll and carotenoid content was low relative to anthocyanins (Fig. 2). Therefore, differences in the type and content of anthocyanins were considered to be the possible reason for the different colours of L. chinense var. rubrum leaves.

At the metabolic level, anthocyanin biosynthesis may be the main pathway involved in leaf pigmentation (Additional file 4). We established that the total anthocyanin content of GL was lower than that of ML and PL and the change in the trend of anthocyanin content corresponded with the change in leaf colour. The contents of cyanidin 3-O-glucoside, cyanidin O-syringic acid, cyanidin 3,5-O-diglucoside, pelargonidin, petunidin 3-O-glucoside, and peonidin 3-sophoroside-5-glucoside were significantly different in the three leaves while those of peonidin, delphinidin, and cyanidin 3-O-rutinoside were not. Therefore, we speculate that the changes in the levels of these anthocyanins influence the colour of L. chinense var. rubrum leaves.

According to the differential expression of genes involved in anthocyanin synthesis and the difference in the content of various anthocyanins, we speculated the reasons for the different colour of L. chinense var. rubrum leaves. Although CYP75A was upregulated in ML and GL to accumulate the raw materials needed for anthocyanin synthesis, anthocyanin was converted into (-)-epicatechin due to the upregulation of ANR. Moreover, the downregulation of the UFGT gene ensured that anthocyanin could not be converted into a stable form, resulting in reduced anthocyanin content in the leaves. The UFGT gene in the anthocyanin biosynthesis pathway is the key to the formation of different types of anthocyanins [51]. In Vitis vinifera, the upregulation of UFGT genes led to the accumulation of anthocyanins [52]. Similar observations were made regarding UFGT expression and anthocyanin accumulation in Malus pumila Mill [53], Litchi chinensis [54], and mango [55]. Therefore, we speculate that UFGT genes contribute significantly to coloration, but the specific regulatory mechanism of these genes needs to be further verified.

In addition to structural genes, it is well known that transcription factors play an essential role in regulating the overall activity of flavonoid biosynthesis. bHLH, MYB, and WD40 proteins are the three main families responsible for regulating anthocyanin biosynthesis genes [56]. The results of qRT-PCR (Fig. 6B) and co-expression analysis (Additional file 14) demonstrated that the expressions of AP2-Like2234, bZIP3720, bHLH, and WD40 were upregulated in PL, correlating strongly with the late synergistic gene UFGT. We speculated that together; they played an important role in regulating the expression of anthocyanin synthesis genes in L. chinense var. rubrum.

At the same time, they may form ternary complexes of MBW to regulate the synthesis of anthocyanins [44, 57]. The MBW complex regulatory structural gene has been confirmed in Petunia and Arabidopsis thaliana and was shown to participate in the later steps of the anthocyanin and condensed tannin biosynthesis pathway [58,59,60]. PpMYB forms the MBW complex and especially activates UFGT to regulate the biosynthesis of anthocyanins [61, 62]. Arabidopsis WD-repeat/Mybs/bHLH complex, including DFR, LDOX, and UF3GT, mainly regulates the expression of ‘late’ anthocyanin biosynthesis genes [44]. Other transcription factors have also been implicated in this regulation: the AP2-Like gene in Arabidopsis [63] and Solanum melongena [64] the MADS-box protein in Morellarubra (red bayberry) and Ficus carica L [65] and the bZIP transcription factor in Raphanus sativus L (radish) [66]. These results indicated that these transcription factors might regulate the accumulation of anthocyanins by controlling the expression of differentially expressed ANR, CYP75A, and UFGT genes. In previous studies, PpERF3 was shown to interact with PpMYB114 and PpbHLH3 to enhance the expression of PpUFGT [67]; FaRAV1 directly bound to and activated GT1 promoter to regulate anthocyanin accumulation [68]; PybZIPa promoted anthocyanin biosynthesis by regulating PyMYB114, PyMYB10, and PyBBX22 as well as PyUFGT promoters [69]; FaMADS1 played a negative role in the accumulation of anthocyanins in strawberry fruits by inhibiting structural genes [70]. It has been proved that transcription factors play a role in regulating the expression of structural genes, but the relationship with anthocyanin biosynthesis needs further experimental verification.


In this study, the transcriptome and metabolome of three-coloured L. chinense var. rubrum leaves were sequenced and analyzed for the first time. We observed the content and dynamic changes in the levels of cyanidin 3-O-glucoside, cyanidin O-syringic acid, cyanidin 3,5-O-diglucoside, pelargonidin, petunidin 3-O-glucoside, and peonidin 3-sophoroside-5-glucoside influenced the colour variation in L. chinense var. rubrum leaves. Nine structural genes, such as ANR, CYP75A, and UFGT, and nine transcription factors, such as MYB, MADS-box, AP2-Like, bZIP, WD40, and bHLH were identified by KEGG database annotation and DEG analysis.

We combined the metabolome and transcriptome data to explore the molecular mechanism underlying L. chinense var. rubrum leaf coloration. By comparing ML, PL, and GL leaves, we speculate that significant upregulation of the UFGT gene is associated with the accumulation of anthocyanins. Therefore, these results may contribute to genetic modification or selection to further enhance the ornamental quality of L. chinense var. rubrum.

Materials and methods

Plant material

In this study, L. chinense var. rubrum plants were provided by Hunan Mid-subtropical Quality Plant Breeding and Utilization Engineering Technology Research Center, Hunan Agricultural University. We selected accession number Xiang S-SV-LC-032-2012-1 as the plant with mosaic leaves accession number Xiang S-SV-LC-032-2012-2 as the plant with purple leaves plant and accession number Xiang S-SV-LC-032-2012-3 as the plant with green leaves. We named them ML, PL, and GL, respectively. Plants were grown in the Garden Flower Base of Hunan Agricultural University, Changsha, Hunan Province, China. (28°12′N, 112°59′E). Samples were immediately frozen in liquid nitrogen after being collected and stored at -80℃ before metabolomic and transcriptomic analyses. The research materials were as shown in Fig. 2. Besides, apical middle and upper leaves per colour were selected; half the samples were sent to Wuhan Netware for metabolome determination and the metabolites were qualitatively and quantitatively analyzed based on the UPLC-Q-trap/MS detection platform and extensively targeted metabolome detection technology. The other half of the samples were sent to Beijing Novogene for transcriptome sequencing. Three biological replicates of GL, ML, and PL were recorded as samples GL1-3, ML1-3, and PL1-3.

Analysis of leaf phenotype

The colour of the three-coloured leaves was measured with a Royal Horticultural Society Colour Chart and spectrophotometer, with seven leaves for each colour system. The brightness L*, the redness a*, and the yellowness b* were measured using the spectrophotometer, and the C* value representing the chroma was calculated by the formula: (CIRG) = (180 − θ)/(L*+C*), where C*=(a*2+b*2)0.5; hue angle θ represents the colour change, θ = arctan (b */a *) [71].

The whole leaf characteristics of the ML, PL, and GL were photographed under scattered light by a digital camera (Canon EOS5D Mark III, Japan). Then the lower epidermis of the leaves was torn off with forceps and the epidermal cells were observed under an eyepiece/objective lenses 10×/20× inverted microscope (Leica Microsystems CMS Gmbh, Germany). The mature leaves were sandwiched in two blades and cut; the cross-section was observed under an eyepiece/objective lenses 10×/20× inverted microscope (Leica Microsystems CMS Gmbh, Germany). The original resolution of all acquired microscopic images was 2048 × 1536.

Chlorophyll content and carotenoid content in GL, ML, and PL of L. chinense var. rubrum were measured directly by the extraction method. After the veins were removed, 0.1 g of each kind of leaf was weighed, followed by the addition of 15 ml of 95% ethanol was added. Every leaf tissue was incubated at 4℃ for extraction in the dark and finally, the supernatant was collected. The absorbance values at 470 nm, 649 nm, and 665 nm were measured with an ultraviolet spectrophotometer (AOE TSD-599, China) and each coloured-leaf variety had six replicates. Anthocyanin was detected by the pH differential method for cyanidin-3-glucoside content in plants. The revised method proposed by Zhang et al. was adopted [72] and the calculation formula is as follows: Ca = 13.95A665 − 6.88649; Cb = 24.96A649 − 7.32A665; Cc = (1000A470 − 2.05Ca -114.8Cb)/248; TA = A*MW*5*100*V/e, where TA stands for total anthocyanin content (mg/100 g, as cyanidin-3-O-glucose equivalent), V stands for final volume (mL), and A = [A510 (pH 1.0) - A700 (pH 1.0)] - [A510 (pH 4.5) - A700 (pH 4.5)]. A molar absorptivity (e) of 26,900 m2.mol− 1 and molecular weight (MW) of 449.2 Da were used according to Wrolstad et al. (1982). Three measurements were taken for every six biological replicates [73].

Metabolic analysis

Approximately 0.1 g of freeze-dried leaf sample approximately was weighed and pulverized with a grinder at 30 Hz for 1.5 min. Leaf metabolites were extracted by adding 1 ml extraction solution (70% of aqueous methanol) to an overnight incubation and 10 min centrifugation at 10,000 g at 4℃. The samples were filtered through a 0.22 μm membrane filter before analysis using an LC-ESI-MS/MS system (HPLC, Shim-pack UFLCSHIMADZU CBM30A system,; MS, Applied Biosystems 4500 Q TRAP, Chromatographic separation was performed on a Waters ACQUITY UPLC HSS T3 column (1.8 μm, 2.1 mm*100 mm) using solvent A (water, 0.04% acetic acid) and solvent B (acetonitrile, 0.04% acetic acid). The gradient program in terms of solvent system was solvent A: solvent B; the gradient program was 95:5 v/v at 0 min, 5:95 v/v at 11.0 min, 5:95 v/v at 12.0 min, 95:5 v/v at 12.1 min and 95:5 v/v at 15.0 min. The flow rate was 0.40 ml.min− 1, the column temperature was 40℃ and the injection volume was 5µL. The effluent was alternately connected to an electrospray-triple quadrupole rod-linear ion trap-mass spectrometer. The electrospray ionization source temperature was 550℃; the ion spray voltage was 5500 V and the CUR was set to 55, 60, and 25.0 psi respectively.

Based on the self-built database MWDB and the public database of metabolite information, the primary and secondary spectral data after mass spectrometric detection were qualitatively analyzed. The repetitive signals of high MW substances such as K+, Na+, and NH3+ were removed. The structure analysis of metabolites was from the Mass Bank (, KNAPSACK (, HMDB (http// [74], MOTODB ( and METLIN ( [75]. The KEGG pathway database ( was used to identify related metabolic pathways. Metabolite quantification was performed using triple and four-stage mass spectrometric multiple reaction monitoring modes (MRM): the four-stage mass spectrometer first screened out the parent ion (Q1) of the target substance, which was further fragmented into fragment ions (Q2) and finally filtered through the three-stage four-stage mass spectrometer to screen out the characteristic fragment ions (Q3) [76].

RNA extraction, library construction, and RNA-seq

Total RNA was extracted from approximately 2 g of L. chinense var. rubrum leaves using Gene Star (Beijing, China). Library preparation and transcriptome sequencing were performed by Novogene Bioinformatics Technology (Beijing, China). The libraries were sequenced using an Illumina HiSeq platform and the raw reads were filtered by the adaptor sequence, lower quality and N-containing reads followed by de novo assembly of the transcriptome using Trinity. The unigene for functional annotation was obtained by aligning the unigene with the GO, KOG, KEGG, Nt, Nr, and Pfam databases to annotate their potential metabolic pathways.

Expression levels of genes related to leaf colour formation in L. chinense var. rubrum were determined using the RSEM software package [77, 78]. The expression abundance of the unigenes was evaluated using the FPKM method [79]. Differential gene expression analyses in the different-coloured leaves were performed using the DESeq R package. DEGs were identified as those genes that had |log2(fold change)|≥2, FDR value < 0.01. GO and KEGG pathway enrichment analysis of the DEGs was done using the GOseq R package-based hypergeometric distribution [80], which can adjust for gene length bias in DEGs.

qRT-PCR analysis

To further confirm the reliability of RNA-seq data in our differential expression analysis, the relative expression of nine structural genes in the flavonoid metabolic pathway was analyzed through qRT-PCR. Primers were designed using Primer Premier 5.0 software and the details are shown in Additional file 15. The β-actin gene was used as an internal reference gene and three biological replicates were set for each biological sample. The expression level of each gene in the list was calculated using the Livak method (delta-delta CT, 2−ΔΔCt)and expressed as the average standard deviation [81].

Integrative metabolomic and transcriptomic analysis

Transcriptomic and metabolomic data for L. chinense var. rubrum leaves with clear differences were used for analysis. Correlation analysis was carried out according to the metabolite content of different colors of leaves in metabolic data and the differential gene expression in transcriptome data.

Figure 1 Biosynthesis pathway of anthocyanin. PAL (phenylalanine ammonia lyase); C4H (cinnamate 4-monooxygenase); 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 (dihydroflflavonol 4-reductase); ANS (anthocyanidin synthase); GT (glucosyltransferases); MT (methyltransferases) and AT (acyltransferases).

Figure 4 Differential metabolites from different leaves. A PCA score plot of three materials and numbers of potential markers for each leaf color. B Venn diagram shows the overlapping and cultivar-specific differential metabolites from ML, PL, and GL.

Figure 5 Differential expression genes in different colors. A Thermal diagram of the correlation coefficient between leaves. The Pearson correlation coefficient is within [-1, 1], and the closer it is to 1 or -1, the stronger the positive/anti-linear relationship. B Venn diagram of DEGs.

Data Availability

All data generated/analyzed during this study are included in this article and its supplementary files. The sequencing data associated with transcription profiles in this study have been deposited in the NCBI SRA database with accession number PRJNA741349( PRJNA741349). The metabolomics data have been uploading in Metabolites database with accession number MTBLS5662 (


L. chinense var. rubrum :

Loropetalum chinense var. rubrum


green leaves


mosaiced leaves


purple leaves


Chlorophyll a


Chlorophyll b


Carotenoid concentration


Total anthocyanin


fresh weight


phenylalanine ammonia-lyase

C4H :

cinnamate 4-hydroxylase

4CL :



chalcone synthase


chalcone isomerase

F3H :

flavanone 3-hydroxylase


anthocyanidin reductase

CYP75A :

flavonoid 3’,5’-hydroxylase


dihydro-flavonol 4-reductase




MYB transcription factor

bHLH :

basic helix-loop-helix transcription factor


WD40 proteins

bZIP :

bZIP transcription factor

AP2-like :



MADS-box transcription factor


Log2 fold change


differentially expressed genes


Gene ontology


eukaryotic Ortholog Groups


Kyoto Encyclopedia of Gene and Genomes


NCBI nucleotide sequences


NCBI nonredundant protein


Protein family


Molecular weight


Multiple reaction monitoring


Curtain gas


Met ware database


  1. Zhang W, Zhang C, Li M, Du W, Shao H, Yang S, Feng Y, Chen H. Phytochemical and chemotaxonomic study on Loropetalum chinense (R. Br.) Oliv. Biochemical Systematics and Ecology 2018, 81:80–82.

  2. Zhou G, Yu H, Lu C, Zhou Z. Primary Study on Bacteriostatic Active Compound from Leaves of Loropetalum chinense var.rubrum Yieh. Food Sciense 2007(06):74–77.

  3. Tang H, Zheng Q, Liang T, Zhong T, Huang l, Ge G, Sun B. Analysis of Volatile Oils in Leaves of Loropetalum chinense and L.chinense var. rubrum Using Simultanuous Distillation and Solvent Ex- traction (SDE) and GC-MS Journal of Anhui Agri 2011, 39(26):15985–15987 + 15990.

  4. Tadmor Y, Burger J, Yaakov I, Feder A, Libhaber SE, Portnoy V, Meir A, Tzuri G, Saar U, Rogachev I, et al. Genetics of Flavonoid, Carotenoid, and Chlorophyll Pigments in Melon Fruit Rinds. J Agric Food Chem. 2010;58(19):10722–8.

    Article  CAS  PubMed  Google Scholar 

  5. Tanaka Y, Sasaki N, Ohmiya A. Biosynthesis of plant pigments: anthocyanins, betalains and carotenoids. Plant J. 2008;54(4):733–49.

    Article  CAS  PubMed  Google Scholar 

  6. Harborne JB, Williams CA. Anthocyanins and other flavonoids. Natural product reports 2001, 18(3):310–333.

  7. Tanaka Y, Brugliera F, Kalc G, Senior M, Dyson B, Nakamura N, Katsumoto Y, Chandler S. Flower Color Modification by Engineering of the Flavonoid Biosynthetic Pathway: practical perspectives. Bioscience Biotechnol Biochem. 2010;74(9):1760–9.

    Article  CAS  Google Scholar 

  8. Yan H-F, Zhao Z-G. Extraction of anthocyanins from sugarcane rind and co-pigmentation effect of co-pigments on anthocyanins. Modern Food Science and Technology 2017(No.9):176–183.

  9. Rashid J, Farideh B. Stability of blueberry (Cornus mas - Yulyush) anthocyanin pigment under pH and co-pigment treatments. International Journal of Food Properties 2017, 20(9):2128–2133.

  10. Liu X, Liu C, Lu X, Tong Z, Tao C, Yuan X. Comprehensive transcriptome analyses of Carbohydrate Metabolism and Flavonoid Biosynthesis in Blueberry (Vaccinium corymbosum) during Fruit Maturation. Int J Agric Biology. 2020;24(1):101–11.

    CAS  Google Scholar 

  11. Lin Q, Zhong Q, Zhang Z. Comparative transcriptome analysis of genes involved in anthocyanin biosynthesis in the pink-white and red fruits of chinese bayberry (Morella rubra). Sci Hort. 2019;250:278–86.

    Article  CAS  Google Scholar 

  12. Deng C, Li S, Feng C, Hong Y, Huang H, Wang J, Wang L, Dai S. Metabolite and gene expression analysis reveal the molecular mechanism for petal colour variation in six Centaurea cyanus cultivars. Plant Physiol Biochem. 2019;142:22–33.

    Article  CAS  PubMed  Google Scholar 

  13. Li L, Zhai Y, Luo X, Zhang Y, Shi Q. Comparative transcriptome analyses reveal genes related to pigmentation in the petals of red and white Primula vulgaris cultivars. Physiol Mol Biology Plants. 2019;25(4):1029–41.

    Article  CAS  Google Scholar 

  14. Zhang C, Li X, Zhan Z, Cao L, Zeng A, Chang G, Liang Y. Transcriptome Sequencing and Metabolism Analysis Reveals the role of Cyanidin Metabolism in Dark-red Onion (Allium cepa L.) Bulbs. Scientific Reports 2018, 8.

  15. Asem ID, Imotomba RK, Mazumder PB, Laishram JM. Anthocyanin content in the black scented rice (Chakhao): its impact on human health and plant defense. Symbiosis 2015, 66(1):47–54.

  16. Sudheeran PK, Feygenberg O, Maurer D, Alkan N. Improved Cold Tolerance of Mango Fruit with Enhanced Anthocyanin and Flavonoid Contents. Molecules 2018, 23(7).

  17. Nakabayashi R, Yonekura-Sakakibara K, Urano K, Suzuki M, Yamada Y, Nishizawa T, Matsuda F, Kojima M, Sakakibara H, Shinozaki K, et al. Enhancement of oxidative and drought tolerance in Arabidopsis by overaccumulation of antioxidant flavonoids. Plant J. 2014;77(3):367–79.

    Article  CAS  PubMed  Google Scholar 

  18. Mattioli R, Francioso A, Mosca L, Silva P. Anthocyanins: A Comprehensive Review of Their Chemical Properties and Health Effects on Cardiovascular and Neurodegenerative Diseases. Molecules 2020, 25(17).

  19. Xu D, Pan Y, Chen J. Chemical Constituents, Pharmacologic Properties, and Clinical Applications of Bletilla striata. Frontiers in Pharmacology 2019,10.

  20. Luo Q, Liu R, Zeng L, Wu Y, Jiang Y, Yang Q, Nie Q. Isolation and molecular characterization of NtMYB4a, a putative transcription activation factor involved in anthocyanin synthesis in tobacco. Gene 2020, 760.

  21. Saito K, Yonekura-Sakakibara K, Nakabayashi R, Higashi Y, Yamazaki M, Tohge T, Fernie AR. The flavonoid biosynthetic pathway in Arabidopsis: structural and genetic diversity. Plant Physiol Biochem. 2013;72:21–34.

    Article  CAS  PubMed  Google Scholar 

  22. Holton TA, Brugliera F, Tanaka YJTPJ. Cloning and expression of flavonol synthase from Petunia hybrida. 2010, 4(6):1003–1010.

  23. Sonmez DA, Urun I, Alagoz D, Attar SH, Dogu Z, Yesil B, Wozniak A, Labudda M, Zydlik Z, Zydlik P et al. Phenylalanine ammonialyase and invertase activities in strawberry fruit during ripening progress. Acta Horticulturae 2021(No.1309):947–954.

  24. Peng Y, Thrimawithana AH, Cooney JM, Jensen DJ, Espley RV, Allan AC. The proanthocyanin-related transcription factors MYBC1 and WRKY44 regulate branch points in the kiwifruit anthocyanin pathway. Scientific Reports 2020, 10(1).

  25. Shin D-H, Choi M-G, Kang C-S, Park C-S, Choi S-B, Park Y-I. Overexpressing the wheat dihydroflavonol 4-reductase gene TaDFR increases anthocyanin accumulation in an Arabidopsis dfr mutant. Genes & Genomics. 2016;38(4):333–40.

    Article  CAS  Google Scholar 

  26. Zhang H-L, Zhao X-J, Zhang J-P, Yang B, Yu Y-H, Liu T-F, Nie B-H, Song B-T. Functional analysis of an anthocyanin synthase gene StANS in potato. Sci Hort. 2020;272:109569–9.

    Article  CAS  Google Scholar 

  27. Zapletalova MK, Vanduchova A, Spicakova A, Anzenbacherova E, Anzenbacher P. Interaction of anthocyanins with human liver microsomal cytochromes P450. Biomedical Papers-Olomouc. 2013;157:56–S59.

    Google Scholar 

  28. Khoo HE, Azlan A, Tang ST, Lim SM. Anthocyanidins and anthocyanins: colored pigments as food, pharmaceutical ingredients, and the potential health benefits. Food & Nutrition Research. 2017;61:1–21.

    Article  Google Scholar 

  29. Sasaki N, Nishizaki Y, Ozeki Y, Miyahara T. The role of acyl-glucose in anthocyanin modifications. Molecules. 2014;19(11):18747–66.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Morita Y, Hoshino A, Kikuchi Y, Okuhara H, Ono E, Tanaka Y, Fukui Y, Saito N, Nitasaka E, Noguchi H, et al. Japanese morning glory dusky mutants displaying reddish-brown or purplish-gray flowers are deficient in a novel glycosylation enzyme for anthocyanin biosynthesis, UDP-glucose: anthocyanidin 3-O-glucoside-2’’-O-glucosyltransferase, due to 4-bp insertions in the gene. The Plant journal: for cell and molecular biology. 2005;42(3):353–63.

    Article  CAS  PubMed  Google Scholar 

  31. Nogales-Bueno J, Baca-Bocanegra B, Jose Heredia F, Miguel Hernandez-Hierro J. Phenolic compounds extraction in enzymatic macerations of grape skins identified as low-level extractable total anthocyanin content. J Food Sci. 2020;85(2):324–31.

    Article  CAS  PubMed  Google Scholar 

  32. Suzuki H, Sawada Sy, Watanabe K, Nagae S, Yamaguchi M-A, Nakayama T, Nishino T. Identification and characterization of a novel anthocyanin malonyltransferase from scarlet sage (Salvia splendens) flowers: an enzyme that is phylogenetically separated from other anthocyanin acyltransferases. The Plant journal: for cell and molecular biology 2004, 38(6):994–1003.

  33. Liu X, Huang Y, Qiu Z, Gong H. Comparative transcriptome analysis of differentially expressed genes between the fruit peel and flesh of the purple tomato cultivar ‘Indigo Rose’. Volume 15. Plant Signaling & Behavior; 2020. 6.

  34. Wang P, Chen S, Gu M, Chen X, Chen X, Yang J, Zhao F, Ye N. Exploration of the Effects of Different Blue LED Light Intensities on Flavonoid and Lipid Metabolism in Tea Plants via Transcriptomics and Metabolomics.International Journal of Molecular Sciences 2020, 21(13).

  35. Yang K-B, Li Y, Wang S-N, Xu X-R, Sun H-Y, Zhao H-S, Li X-P, Gao Z-M. Genome-wide identification and expression analysis of the MYB transcription factor in Moso bamboo (Phyllostachys edulis). PeerJ; 2019. p. 6.

  36. Wang L, Huang Q, Zhang L, Wang Q, Liang L, Liao B. Genome-Wide Characterization and Comparative Analysis of MYB Transcription Factors in Ganoderma Species. G3-Genes Genomes Genetics 2020, 10(8):2653–2660.

  37. Roy S. Function of MYB domain transcription factors in abiotic stress and epigenetic control of stress response in plant genome. Plant Signaling & Behavior 2016, 11(1).

  38. Zhang Y, Chen G, Dong T, Pan Y, Zhao Z, Tian S, Hu Z. Anthocyanin Accumulation and Transcriptional Regulation of Anthocyanin Biosynthesis in Purple Bok Choy (Brassica rapa var. Chinensis). J Agric Food Chem. 2014;62(51):12366–76.

    Article  CAS  PubMed  Google Scholar 

  39. Omer S, Kumar S, Khan BM. Over-expression of a subgroup 4 R2R3 type MYB transcription factor gene from Leucaena leucocephala reduces lignin content in transgenic tobacco. Plant Cell Rep. 2013;32(1):161–71.

    Article  CAS  PubMed  Google Scholar 

  40. Junhui Y, Biao W, Yunpeng Z, Luming Y, Linjing C, Tianlong W. The soybean R2R3 MYB transcription factor GmMYB100 negatively regulates plant flavonoid biosynthesis. Plant Mol Biol. 2015;89(1–2):35–48.

    Google Scholar 

  41. Zhang S, Ma P, Yang D, Li W, Liang Z, Liu Y, Liu F. Cloning and Characterization of a Putative R2R3 MYB Transcriptional Repressor of the Rosmarinic Acid Biosynthetic Pathway from Salvia miltiorrhiza. Plos One 2013, 8(9).

  42. An X-H, Tian Y, Chen K-Q, Wang X-F, Hao Y-J. The apple WD40 protein MdTTG1 interacts with bHLH but not MYB proteins to regulate anthocyanin accumulation. J Plant Physiol. 2012;169(7):710–7.

    Article  CAS  PubMed  Google Scholar 

  43. Albert NW. Subspecialization of R2R3-MYB Repressors for Anthocyanin and Proanthocyanidin Regulation in Forage Legumes. Frontiers in Plant Science 2015, 6.

  44. Gonzalez A, Zhao M, Leavitt JM, Lloyd AM. Regulation of the anthocyanin biosynthetic pathway by the TTG1/bHLH/Myb transcriptional complex in Arabidopsis seedlings. Plant J. 2008;53(5):814–27.

    Article  CAS  PubMed  Google Scholar 

  45. Liang Z, Sang M, Fan P, Wu B, Wang L, Yang S, Li S. CIELAB Coordinates in Response to Berry Skin Anthocyanins and Their Composition in Vitis. Journal of Food Science 2011, 76(3):C490-C497.

  46. Kanehisa M. Enzyme Annotation and Metabolic Reconstruction using KEGG. Methods in molecular biology. (Clifton NJ). 2017;1611:135–45.

    CAS  Google Scholar 

  47. Sun Y, Huang H, Meng L, Hu K, Dai S-L. Isolation and functional analysis of a homolog of flavonoid 3 ‘,5 ‘-hydroxylase gene from Pericallis x hybrida. Physiol Plant. 2013;149(2):151–9.

    Article  CAS  PubMed  Google Scholar 

  48. Niu T, Li C, Yang B, Zhang P, Fan W, Wen P. Characterization, expression and function analysis of anthocyanidin reductase gene from Vitis vinifera L. cv. Cabernet Sauvignon under UV-C irradiation. Biotechnol Biotechnol Equip. 2021;35(1):43–52.

    Article  CAS  Google Scholar 

  49. Yeon L, Hye Ryon Y, Yong Sook P, Jang Ryol L, Won-II C, Giltsu C. Reciprocal regulation of Arabidopsis UGT78D2 and BANYULS is critical for regulation of the metabolic flux of Anthocyanidins to Condensed Tannins in developing seed Coats. J Plant Biology. 2005;48(4):356–70.

    Article  Google Scholar 

  50. Terrier N, Torregrosa L, Ageorges A, Vialet S, Verries C, Cheynier V, Romieu C. Ectopic expression of VvMybPA2 promotes Proanthocyanidin Biosynthesis in Grapevine and suggests additional targets in the pathway. Plant Physiol. 2009;149(2):1028–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Muhammad N, Luo Z, Yang M, Li X, Liu Z, Liu M. The joint role of the late anthocyanin biosynthetic UFGT-encoding genes in the flowers and fruits coloration of horticultural plants. Scientia Horticulturae 2022, 301.

  52. Kobayashi S, Ishimaru M, Ding CK, Yakushiji H, Goto N. Comparison of UDP-glucose: flavonoid 3-O-glucosyltransferase (UFGT) gene sequences between white grapes (Vitis vinifera) and their sports with red skin.Plant science: an international journal of experimental plant biology 2001, 160(3):543–550.

  53. Chen W, Zhang M, Zhang G, Li P, Ma F. Differential Regulation of Anthocyanin Synthesis in Apple Peel under Different Sunlight Intensities. International Journal of Molecular Sciences 2019, 20(23).

  54. Xiao-Jing L, Jie-Qiong Z, Zi-Chen W, Biao L, Xu-Ming H, Yong-Hua Q, Hui-Cong W, Gui-Bing H. Functional characterization of a glucosyltransferase gene, LcUFGT1, involved in the formation of cyanidin glucoside in the pericarp of Litchi chinensis. Physiol Plant. 2016;156(2):139–49.

    Article  Google Scholar 

  55. Kanzaki S, Kamikawa S, Ichihi A, Tanaka Y, Shimizu K, Koeda S, Utsunomiya N. Isolation of UDP: flavonoid 3-O-glycosyltransferase (UFGT)-like genes and expression analysis of genes Associated with Anthocyanin Accumulation in Mango ‘Irwin’ skin. Hortic J. 2019;88(4):435–43.

    Article  CAS  Google Scholar 

  56. Zhao L, Gao L, Wang H, Chen X, Wang Y, Yang H, Wei C, Wan X, Xia T. The R2R3-MYB, bHLH, WD40, and related transcription factors in flavonoid biosynthesis. Funct Integr Genom. 2013;13(1):75–98.

    Article  CAS  Google Scholar 

  57. Zhou L-L, Shi M-Z, Xie D-Y. Regulation of anthocyanin biosynthesis by nitrogen in TTG1-GL3/TT8-PAP1-programmed red cells of Arabidopsis thaliana. Planta 2012, 236(3):825–837.

  58. Hichri I, Barrieu F, Bogs J, Kappel C, Delrot S, Lauvergeat V. Recent advances in the transcriptional regulation of the flavonoid biosynthetic pathway. Journal of Experimental Botany 2011, 62(8):2465–2483.

  59. Trainin T, Harel-Beja R, Bar-Ya’akov I, Ben-Simhon Z, Yahalomi R, Borochov-Neori H, Ophir R, Sherman A, Doron-Faigenboim A, Holland D. Fine Mapping of the “black” Peel Color in Pomegranate (Punica granatum L.) Strongly Suggests That a Mutation in the Anthocyanidin Reductase (ANR) Gene Is Responsible for the Trait. Frontiers in Plant Science 2021, 12.

  60. Wang H, Fan W, Li H, Yang J, Huang J, Zhang P. Functional Characterization of Dihydroflavonol-4-Reductase in Anthocyanin Biosynthesis of Purple Sweet Potato Underlies the Direct Evidence of Anthocyanins Function against Abiotic Stresses. Plos One 2013, 8(11).

  61. Borevitz JO, Xia Y, Blount J, Dixon RA, Lamb C. Activation tagging identifies a conserved MYB Regulator of Phenylpropanoid Biosynthesis. Plant Cell. 2000;12(12):2383–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Feng S, Wang Y, Yang S, Xu Y, Chen X. Anthocyanin biosynthesis in pears is regulated by a R2R3-MYB transcription factor PyMYB10. Planta. 2010;232(1):245–55.

    Article  CAS  PubMed  Google Scholar 

  63. Chen Y, Wu P, Zhao Q, Tang Y, Chen Y, Li M, Jiang H, Wu G. Overexpression of a Phosphate Starvation Response AP2/ERF Gene From Physic Nut in Arabidopsis Alters Root Morphological Traits and Phosphate Starvation-Induced Anthocyanin Accumulation. Frontiers in Plant Science 2018, 9.

  64. Li D, He Y, Li S, Shi S, Li L, Liu Y, Chen H. Genome-wide characterization and expression analysis of AP2/ERF genes in eggplant (Solanum melongena L). Plant Physiol Biochem. 2021;167:492–503.

    Article  CAS  PubMed  Google Scholar 

  65. Li J, Ma N, An Y, Wang L. FcMADS9 of fig regulates anthocyanin biosynthesis. Scientia Horticulturae 2021, 278.

  66. Fan L, Xu L, Wang Y, Tang M, Liu L. Genome- and Transcriptome-Wide Characterization of bZIP Gene Family Identifies Potential Members Involved in Abiotic Stress Response and Anthocyanin Biosynthesis in Radish (Raphanus sativus L.). International Journal of Molecular Sciences 2019, 20(24).

  67. Ni J-B, Bai S-L, Zhao Y, Qian M-J, Tao R-Y, Yin L, Gao L, Teng Y-W. Ethylene response factors Pp4ERF24 and Pp12ERF96 regulate blue light-induced anthocyanin biosynthesis in “Red Zaosu” pear fruits by interacting with MYB114. Plant Mol Biol. 2019;99(1–2):67–78.

    Article  CAS  PubMed  Google Scholar 

  68. Zhang Z, Shi Y, Ma Y, Yang X, Yin X, Zhang Y, Xiao Y, Liu W, Li Y, Li S, et al. The strawberry transcription factor FaRAV1 positively regulates anthocyanin accumulation by activation of FaMYB10 and anthocyanin pathway genes. Plant Biotechnol J. 2020;18(11):2267–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Liu H, Su J, Zhu Y, Yao G, Allan AC, Ampomah-Dwamena C, Shu Q, Lin-Wang K, Zhang S, Wu J. The involvement of PybZIPa in light-induced anthocyanin accumulation via the activation of PyUFGT through binding to tandem G-boxes in its promoter. Horticulture Research 2019, 6.

  70. Lu W, Chen J, Ren X, Yuan J, Han X, Mao L, Ying T, Luo Z. One novel strawberry MADS-box transcription factor FaIVIADS1 a acts as a negative regulator in fruit ripening. Sci Hort. 2018;227:124–31.

    Article  CAS  Google Scholar 

  71. Feng C, Chen M, Xu C-J, Bai L, Yin X-R, Li X, Allan AC, Ferguson IB. Chen K-s: Transcriptomic analysis of Chinese bayberry (Myrica rubra) fruit development and ripening using RNA-Seq. Bmc Genomics 2012, 13.

  72. Zhang Y-L, Fang Z-Z, Ye X-F, Pan S-L. Identification of candidate genes involved in anthocyanin accumulation in the peel of jaboticaba (Myrciaria cauliflora) fruits by transcriptomic analysis. Gene. 2018;676:202–13.

    Article  CAS  PubMed  Google Scholar 

  73. Romero I, Sanchez-Ballesta MT, Maldonado R, Escribano MI, Merodio C. Anthocyanin, antioxidant activity and stress-induced gene expression in high CO2-treated table grapes stored at low temperature. Journal of Plant Physiology 2008, 165(5):522–530.

  74. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, et al. HMDB 3.0-The human metabolome database in 2013. Nucleic Acids Res. 2013;41(D1):D801–7.

    Article  CAS  PubMed  Google Scholar 

  75. Zhu Z-J, Schultz AW, Wang J, Johnson CH, Yannone SM, Patti GJ, Siuzdak G. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat Protoc. 2013;8(3):451–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Chen Y, Zhang R, Song Y, He J, Sun J, Bai J, An Z, Dong L, Zhan Q, Abliz Z. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer. Analyst 2009, 134(10):2003–2011.

  77. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. Bmc Bioinformatics 2011,12.

  78. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology 2010, 11(10).

  79. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8.

    Article  CAS  PubMed  Google Scholar 

  80. Mao X, Cai T, Olyarchuk JG, Wei L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinf (Oxford England). 2005;21(19):3787–93.

    Article  CAS  Google Scholar 

  81. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2–∆∆CT method. Volume 25. Methods; 2001. pp. 402–8. 4.

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This study was conducted by Hunan Provincial Science and Technology Plan (Grant No.2018TP2007), Hunan Agriculture Establishment, Research and Cultivation of Universities’ Shuang Yi Current‘(Grant No. SYL201802026 and SYL2019012), Hunan province education department project(Grant No.18B124) and Hu Nan agricultural university the third batch of major scientific research projects and a New team cultivation project (Grant No.201909), Open Project of Horticulture Discipline of Hunan Agricultural University (Grant No. 2021YYXK001), The Forestry Science and Technology Innovation Foundation of Hunan Province for Distinguished Young Scholarship, (Grant No.XLKJ202205), National Innovation and Entrepreneurship Training Program for College Students(Grant No.202112653017X), The Found of Changsha Municipal Science and Technology Bureau (Grant No.KQ2202227). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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Authors and Affiliations



X.Z., L.Z., and D.M.Z collected the sample and conceived and designed the study. X.Z., L.Z., D.D.S., and W.D.L the genome annotation and functional genomic analysis. X.F.W., Q.R.C., W.Q.C., L.X., and F.X.C performed the data analysis of the metabolome. X.Z., X.Y.Y., Y.L.L., and D.L.Z wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding authors

Correspondence to Dongling Zhang, Xiaoying Yu or Yanlin Li.

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Additional file 1: Table S1

. Detection Data of Various Physiological Characteristics of ML, PL, and GL Tricolor Leaves

Additional file 2: Table S2.

A list of 207 flavonoid metabolites identified in Loropetalum chinense var. rubrum

Additional file 3: Table S3.

Differential metabolites between GL, ML, and PL

Additional file 4: Fig. S1.

KEGG annotation of putative proteins. The x-axis indicates the percentage of the number of genes annotated to the pathway out of the total number of genes annotated. The y-axis indicates the name of the KEGG metabolic pathway. A KEGG pathway analysis between GL and ML. B KEGG pathway analysis of between GL and PL. C KEGG pathway analysis between ML and PL

Additional file 5: Table S4.

Statistical analysis of L. chinense var. rubrum reads in 9 libraries (Each sample was repeated three times)

Additional file 6: Table S5.

The length distribution of assembled unigenes

Additional file 7: Table S6.

The unigenes were successfully annotated to the seven databases

Additional file 8: Table S7.

Number of DEGs in tricolor leaves

Additional file 9: Fig. S2.

GO classification of DEGs. A GO functional classification of DEGs between GL vs. ML. B GO functional classification of DEGs between GL vs. PL. C GO functional classification of DEGs between ML vs. PL

Additional file 10: Table S8.

KEGG annotation of different metabolic pathways

Additional file 11: Table S9.

FPKM of key anthocyanin biosynthesis-related genes in leaves of L. chinense var. rubrum

Additional file 12: Table S10.

qRT-PCR data for structural and transcriptional factors

Additional file 13: Table S11.

R2 of key anthocyanin biosynthesis-related

Additional file 14: Fig. S3.

Correlation analysis of transcription factors and structural genes

Additional file 15: Table S12.

List of primers used in this study

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Zhang, X., Zhang, L., Zhang, D. et al. Comprehensive analysis of metabolome and transcriptome reveals the mechanism of color formation in different leave of Loropetalum Chinense var. Rubrum. BMC Plant Biol 23, 133 (2023).

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