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. 2021 Mar 28;6(13):9028–9038. doi: 10.1021/acsomega.1c00059

Joint Transcriptomic and Metabolic Analysis of Flavonoids in Cyclocarya paliurus Leaves

Xiaoling Sheng †,‡, Huanwei Chen §, Jianmei Wang |, Yongli Zheng ⊥, Yueling Li ‡, Zexin Jin ‡, Junmin Li ‡,*
PMCID: PMC8028134  PMID: 33842773

Abstract

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Flavonoids are a class of commonly occurring natural compounds in the plant kingdom with various biological activities. This study compares the content of flavonoids in Cyclocarya paliurus at different developmental stages to better inform the selection of the optimal picking period. Thus, we analyzed the transcriptome and metabolome of C. paliurus at different developmental stages. The transcriptome analysis revealed 44 genes involved in the biosynthesis of flavonoids in C. paliurus, with 10 differentially expressed genes across the four different developmental stages. The metabolites were separated and identified by a combination of chromatography and mass spectrometry, followed by multi-reaction monitoring mode analysis of triple quadrupole mass spectrometry for complete metabolite quantification. In the flavonoid synthesis pathway, a total of 137 differential flavonoids were detected. The joint transcriptome and metabolome analysis showed that the expression trends in differential metabolites and genes were significantly related. Four MYB transcription factors and two bHLH transcription factors that are closely related to flavonoid biosynthesis were identified. The regulation network of flavonoid biosynthesis in C. paliurus was thus established, providing guidance for follow-up research.

Introduction

Cyclocarya paliurus is a rare tree species endemic to subtropical China.1 Leaves of C. paliurus can be used to make tea, which has many benefits to human health.2 In the field of traditional Chinese medicine, C. paliurus leaves are used to treat hypertension, hypercholesterolemia, hyperglycemia, and other diseases and to improve the immune system function.3 A variety of active substances have been identified in this species, including polysaccharides, triterpenes, and flavonoids, which can reduce blood sugar,4 blood pressure, and blood lipid levels5 and possess anticancer,6 antioxidant, and anti-aging effects, among other benefits. Evaluating the compound compositions and the gene regulation network of the main active substances in C. paliurus would benefit the application and development of this species.

Flavonoids are natural bioactive substances that exist at high content levels in plants, including in vegetables, fruits, cereals, and other dietary elements, and it has been widely examined in recent years because of its preventative value as an antioxidant with free radical scavenging capacity and its antiviral, anti-inflammatory, and cardiovascular protection effects.7 It is also reported that flavonoids can be used as natural pancreatic lipase inhibitors, and the degree of inhibition is significantly related to the total flavonoid content.8 Flavonoids in C. paliurus leaves have been verified to be closely related to the effects of the species in lowering blood fat, blood sugar, and blood pressure and improving immunity.9 In response to liver damage caused by carbon tetrachloride, the flavonoids in C. paliurus can effectively reduce oxidative stress and protect mice from acute liver injury. Flavonoids from C. paliurus leaves are effective dietary supplements and can be used to prevent acute liver injury.10 Experiments have shown that C. paliurus leaves with a high total flavonoid content, especially leaves with high quercetin-3-O-glucuronic acid and kaempferol-3-O-glucuronic acid content, administered to streptozotocin-induced diabetic mice significantly lowered blood sugar, suggesting that C. paliurus may be a source for alternative therapies for the treatment of diabetes.11

Flavonoids in plants are mainly created through phenylpropanoid biosynthesis,12 which is catalyzed by a series of key enzymes, including chalcone synthase (CHS), naringenin 3-dioxygenase (F3H), shikimate hydroxycinnamoyltransferase (HCT), chalcone enzyme (CHI), dihydroflavonol 4-reductase (DFR), and other enzymes.13 Recently, UDP-glycosyltransferase and O-methyltransferase were verified as the key enzymes involved in catalyzing the glycosylation of flavonoids in Tartary buckwheat.14 In addition, four major types of transcription factors, in the MADS, WRKY, MYB, and bHLH families, may be involved in the regulation of flavonoid synthesis in Ginkgo biloba.12,15 However, the biosynthetic pathways of flavonoids and their regulation in C. paliurus are currently unclear.

With the rapid development of sequencing technology, transcriptome analysis can be used efficiently.16 As an effective technology for mining gene information, transcriptome analysis has been applied to wheat, barley, potato, tea tree, and other species.17 Metabolomics, as a new set of systems biology tools, can characterize different physiological states of plants under external environmental stimulation.18 For example, the combined analysis of transcriptomes and metabolomes has been successfully used to reveal the biosynthetic regulation pathways of flavonoids in Actinidia arguta, clarifying the relationship between the genotype and the phenotype.19 In this study, we used the joint analysis of transcriptomic and metabolomic data to reveal the composition and biosynthesis of flavonoids and the key candidate structural and regulatory genes related to flavonoid biosynthesis in C. paliurus across four different developmental stages.

Results and Discussion

Differences in Total Flavonoid Content among C. paliurus Leaves at Different Developmental Stages

During the development of C. paliurus leaves, the total flavonoid content in the leaves of C. paliurus at the F3 developmental stage was significantly higher than those at the F2 and F4 stages, which were in turn significantly higher than that at the F1 stage (Figures 1 and 2), indicating an increase with the development of leaves, followed by a decrease. Across different developmental stages of plants, there are differences in secondary metabolite contents. Krochmal-Marczak et al. found that more flavonols were accumulated in mature leaves of sweet potato throughout the leaf growth period.20 The secondary metabolite contents were related to leaf development and the synthesis rate and the activity of secondary metabolites decreased gradually with the development of leaves and cell proliferation.21 The contents of six catechins were previously shown to be significantly different across five development periods in Camellia sinensis leaves, with the content of total catechins in tender leaves significantly higher than that in mature leaves.21 Thus, differences in the content of different secondary metabolites result in different biological activities across plant tissues.22

Figure 1.

Figure 1

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Leaves of C. paliurus at four different developmental stages (F1, F2, F3, and F4).

Figure 2.

Figure 2

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Content of total flavonoids in the leaves of C. paliurus at four different developmental stages (F1, F2, F3, and F4). Bars show the mean ± standard deviation, and different letters indicate significant differences between different developmental stages.

Differences in the Composition of Flavonoids in C. paliurus Leaves at Different Developmental Stages

Qualitative and quantitative analysis of the metabolites in C. paliurus by using LC–MS/MS and multiple reaction monitoring (MRM) revealed a total of 188 identified flavonoids, including 11 catechin derivatives, 13 anthocyanins, 58 flavones, 44 flavonols, 7 flavonolignans, 19 flavonol C-glycosides, 21 flavanones, 11 isoflavones, and 4 proanthocyanidins (Table S1). The principal component analysis results explaining 35.35 and 15.38% of all variation with the first and second axes (PCA1 and PCA2, respectively) revealed variations in the composition of flavonoids in the leaves of C. paliurus throughout development (Figure 3).

Figure 3.

Figure 3

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Principal component analysis results of the composition of flavonoids in the leaves of C. paliurus at four different developmental stages (F1, F2, F3, and F4). Small letters (a–c) indicate different samples.

Based on the established screening thresholds for differential metabolites, that is, fold change ≥ 2 or fold change ≤ 0.5 and VIP ≥ 1, a total of 137 differential flavonoids among the four different developmental stages were screened (Table S2). The Venn diagram in Figure 4 shows that the differential flavonoids varied among the four developmental stages. From F1 to F2, 25 and 56 flavonoids increased and decreased significantly, respectively. From F2 to F3, 30 flavonoids were significantly up- and down-regulated. From F3 to F4, 48 flavonoids became more highly expressed in F4 whereas 22 flavonoids decreased significantly. Among the 137 differential flavonoids, the three compounds with the largest fold change of increasing flavonoids in the four periods were catechin, prunetin, and kumatakenin. For example, the content of catechin at F4 was 376.4 times of that at F3, while the contents of prunetin and kumatakenin at F3 were 307.5 and 197.8 times of those at F2, respectively (Table S2).

Figure 4.

Figure 4

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Venn diagram of flavonoids in the leaves of C. paliurus shared between different stages (F1, F2, F3, and F4).

In our study, C. paliurus leaves at four different developmental stages exhibited differences in catechin derivatives, which accumulated in mature leaves. Among the 188 flavonoids identified, seven out of eleven catechin derivatives had their highest accumulation levels at the F4 phase. This result indicates that the flavonoid content changes throughout the development of leaves. The total catechin content in C. sinensis leaves decreased with leaf age.23 The accumulation patterns of catechins in C. sinensis’s leaves at different developmental stages differed, and epicatechin gallate (ECG) and epigallocatechin contents increased with leaf age, while the epigallocatechin gallate and catechin gallate contents decreased with leaf age.23 El Senousy et al. found that among Cynara scolymus leaves at three different developmental stages, young leaves were rich in luteolin-7-o-glucoside and luteolin-7-o-acetyl-glucoside, while the old basal leaves contained more luteolin-7-o-rutoside (picroside).24 Li et al. found that when Anji Baicha leaves changed from their yellow-green stage to the white stage and then to the reforestation stage, the concentration of epicatechin decreased as the leaves developed.25

Transcriptome Analysis of C. paliurus Leaves at Different Developmental Stages

A total of 44 genes closely related to structural enzymes and modifying enzymes in the leaves of C. paliurus at four different developmental stages were identified in this study (Figure 5). The structural enzymes, including PAL, C4H, 4CL, CHS, CHI, F3H, FLS, F3′H, F3′5′H, LAR, ANS, ANR, and I2′H, and the later modified enzymes, including UGT75C1 and UGT72E, are involved in biosynthesis of flavonoids in C. paliurus.

Figure 5.

Figure 5

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Heat map of 44 genes related to flavonoid synthesis expressed in the leaves of C. paliurus at the F1, F2, F3, and F4 stages. The relative expression levels of genes are indicated from blue to yellow (low to high) across the F1, F2, F3, and F4 developmental stages.

According to the differential gene screening thresholds, that is, q-value < 0.05 and |fold change| ≥ 2, we identified 10 genes closely related to flavonoid biosynthesis in C. paliurus. The expression levels of PAL-1 (TRINITY_DN87383_c2_g1) and C4H-1 (TRINITY_DN83539_ c2_g6) at the F4 stage were more than 7 and 15 times those at the F3 stage, respectively. The expression of TRINITY_DN85515_c0_g1 (CHS-1) increased first and then decreased throughout the developmental stages, with a peak at the F3 stage. TRINITY_DN88191_c1_g1 (FLS-1) and TRINITY_DN82096_c0_g1 (FLS-2) were highly expressed at the F1 stage, but expressed at extremely low levels in F3 and F4 stages. The expression of TRINITY_DN88519_c1_g1 (DFR-1) at the F3 stage was 47 times lower than that at the F1 stage. The expression of TRINITY_DN93270_c1_g1 (UGT72E-1) increased first and then decreased throughout the development of leaves, with a peak at the F2 stage (Figure 5).

Flavonoid Biosynthesis Pathway in C. paliurus

Based on the transcriptomics data, metabolomics data, KEGG pathway, and previous research,26 the biosynthetic pathway diagram of flavonoids in C. paliurus is constructed in Figure 6. It is mainly composed of 24 small branches, including chrysin, naringenin, apigenin, luteolin, chrysoeriol, acacetin, selgin, tricetin, tricin, 2′-hydroxygenistein, hesperetin, kaempferol, myricetin, eriodictyol, quercetin, catechin, (−)-epicatechin, afzelechin, (−)-epiafzelechin, (−)-epigallocatechin, (+)-gallocatechin, cyanidin, pelargonidin, and delphinidin. Each of these compounds is obtained by a series of enzymatic reactions starting with phenylalanine. The differentially expressed key structural genes, including PAL, C4H, CHS, I2′H, FLS, and DFR, at different developmental stages result in the differential accumulation of flavonoids in C. paliurus leaves during the developmental stages (Figure 6). For example, the high expression of PAL in the F4 stage likely caused the flavonoid chrysin to accumulate in this period. The high expression of C4H and CHS during the F3 and F4 stages might have laid the foundation for the high accumulation of naringenin, an important intermediate in flavonoid synthesis.

Figure 6.

Figure 6

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Pathway of flavonoid biosynthesis in the leaves of C. paliurus. The red coloration of the enzyme names indicates a significant up-regulation from the F1 to the F4 stage in the synthesis of flavonoids, and green indicates a significant down-regulation. The fragments per kilobase of transcript per million reads (FPKM) value of unigenes of the enzymes are also indicated from blue to yellow (low to high) across the F1, F2, F3, and F4 developmental stages. Dotted lines indicate that there are more than two reaction steps in the biosynthesis of those particular flavonoids.

Integrated Transcriptome and Metabolome Analysis

The ten differentially expressed genes were used as a guide to screen the differential accumulation of metabolites that was correlated with correlation coefficients greater than 0.9 (Table S3). The positive transcript–metabolite correlation networks are shown in Figure 7.

Figure 7.

Figure 7

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Co-expression analysis of structural genes and metabolites. The yellow nodes represent structural genes, and the other nodes represent metabolites (different colors indicate that they are associated with different structural genes). Black edges represent positive correlations. (A) Metabolites related to I2′H, PAL-1, UGT72E-2, C4H-1. (B) Metabolites related to FLS-1, FLS-2, DFR-1. (C) Metabolites related to CHS-1, CHS-2, UGT72E-1.

There are 149 positively correlated pairs between the differentially expressed genes PAL-1, C4H-1, I2′H, and UGT72E-2 and 54 differentially accumulated metabolites (Figure 7A), of which three (procyanidin, catechin, and kaempferide) were highly correlated with the expression of these four structural genes. Accumulations of tricin 7-O-hexoside, tricin 7-O-feruloylhexoside, and kaempferol 3-O-rhamnoside (kaempferin) were strongly positively correlated with the expression of PAL-1 and UGT72E-2. PAL-1-, C4H-1-, and UGT72E-2-related genes may mediate the synthesis of ECG, epicatechin-epiafzelechin, and luteolin O-eudesmic acid-O-hexoside. The accumulations of most derivatives of tricetin and apigenin were closely positively correlated with the expression of the three genes PAL-1, I2′H, and UGT72E-2.

There were 68 positively correlated pairs between the differentially expressed genes FLS-1, FLS-2, and DFR and 30 differentially accumulated metabolites (Figure 7B). Most of the derivatives of quercetin and cyanidin were closely related to the expression of two FLS genes. Additionally, the accumulation trends of kaempferol and delphinidin were highly similar to the expression trends of the two FLS genes and one DFR gene.

There were 14 positively correlated pairs between the differentially expressed genes CHS-1 and CHS-2 and nine differentially accumulated metabolites, and 4 positively correlated pairs between the differentially expressed gene UCT72E-1 and four differentially accumulated metabolites (Figure 7C).

Combined transcriptome and metabonomic data analysis of C. paliurus leaves at four different developmental stages revealed seven key flavonoid synthesis enzymes (PAL, C4H, CHS, FLS, DFR, I2′H, UGT72E) to be involved in flavonoid biosynthesis. Phenylalanine ammonia lyase (PAL) converts primary metabolism products into secondary metabolism products through the phenylalanine pathway and is considered to be the first step in catalyzing the phenylalanine pathway, such that the formation of flavonoids depends on the activity of PAL.27 Phenylalanine is transformed into trans-cinnamic acid through the action of PAL in the first step, and cinnamic acid 4-hydroxylase (C4H) and trans-cinnamic acid further catalyze the formation of p-coumaric acid, followed by further synthesis of coenzyme A ester through the action of 4CL.28 CHS provides the first key precursor substance for the flavonoid pathway, chalcone.29 Through the action of the chalcone isomerase (CHI), naringin is obtained.30 Later, FLS mediates the formation of flavonols, such as quercetin, kaempferol, and myricetin, and DFR directs the biosynthesis of anthocyanins.31

Isoflavones utilize naringin as a precursor, undergo a multi-step enzymatic reaction, and are finally catalyzed by 4′-methoxyisoflavone 2′-hydroxylase (I2′H).32 The high expression of isoflavone synthesis I2′H-related genes at the F2 stage hindered the synthesis of the isoflavone 2′-hydroxygenistein, and the low expression of this gene in the F3 stage promoted the accumulation of 2′-hydroxygenistein at the F3 stage, which was three times the level at the F2 stage (Figure 6).

Quercetin, kaempferol, and myricetin are generated under the action of FLS, and DFR regulates the production of cyanidin, pelargonidin, and delphinidin. Under the action of the downstream modifying enzymes FLS and DFR, the high expression of genes in the F1 stage promoted the accumulation of 15 types of flavonoids in this period. Among them, the content of cyanidin increased to 3–23 times that of the low expression period. Notably, the syntheses of three substances were blocked during this period. We hypothesize that the expression of related genes inhibited their syntheses. The synthesis of flavonoid glycosides may also be related to the high expression of two UGT72E-related genes that were highly expressed during the F3 and F4 stages in this period. UDPG plays a very important role in the synthesis of glycoside flavonoids.33 The formation of some glycoside flavonoids in C. paliurus is closely related to UGT72E. In this study, we further utilized the 10 differentially expressed genes involved in these seven key enzymes to guide the construction of an interaction network diagram beginning with 10 differential genes and 137 differentially accumulated metabolites across four different developmental time periods (Figure 7). This analysis revealed the relationship between key genes and secondary metabolites, with 9 compounds that are closely related to the expression of two CHS genes, 29 compounds closely related to the expression of two FLS genes, and 9 compounds closely related to the expression of one DFR gene.

Transcription Factor Analysis

The 10 differentially expressed genes were used as a guide to find 78 transcription factor-related genes with expression levels that were correlated, with correlation coefficients greater than 0.9 (Table S4). In this analysis, 7 MYB, 8 bHLH, 15 MYB-related, 36 AP2/ERF, 4 bZIP, and 8 WRKY genes were closely related to the synthesis of flavonoids. The relationship between 7 MYB genes, 8 bHLH genes, and 15 MYB-related genes and flavonoid synthesis-related enzymes is shown in Figure 8.

Figure 8.

Figure 8

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Co-expression analysis of structural genes and differentially expressed MYB and bHLH transcription factors. Genes guiding the analysis are shown in yellow, pink represents MYB or MYB-related transcription factors, purple represents bHLH transcription factors, red lines represent positive correlations, and green lines represent negative correlations.

MYB20, MYB111, MYB1, and MYB44 were grouped together with AtMYB20, AtMYB111, AtMYB1, and AtMYB44, respectively, while bHLH96 and bHLH66 were grouped together with AtbHLH96 and AtbHLH66, respectively (Figure 9A). As indicated in Figures 8 and 9A, C4H, PAL, and UGT72E may be positively regulated by MYB20, while UGT72E may also be positively regulated by MYB44 and bHLH96. At the same time, bHLH96 may be related to the expression of I2′H, and MYB1 and MYB111 may be related to FLS expression. The MYB1 transcription factor may also be related to the expression of DFR, and bHLH66 may be related to the expression of CHS. The gene heat map of these six genes is shown in Figure 9B.

Figure 9.

Figure 9

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Phylogenetic analysis of differentially expressed genes associated with MYB and bHLH (A). The expression levels of four MYB transcription factors and two bHLH transcription factors in the leaves of C. paliurus at four different developmental stages (B). The relative expression levels of genes are indicated from blue to yellow (low to high) across the F1, F2, F3, and F4 developmental stages.

The biosynthesis of plant flavonoids is mainly regulated by the MYB-bHLH-WD40 ternary complex at the transcriptional level.34 Correlation analysis between structural genes and transcription factors provided guidance in finding key transcription factors that regulate the synthesis of flavonoids in C. paliurus. Through correlation analysis, we found four MYB transcription factors and two bHLH transcription factors that are highly similar to structural genes in terms of their expression patterns. A phylogenetic tree analysis showed that TRINITY DN87586_c5_g1 (CpMYB20) and AtMYB20 are clustered together. When phenylalanine is used as the raw material to synthesize flavonoids and lignin, the two pathways show a competitive relationship.34AtMYB20, AtMYB42, AtMYB43, and AtMYB85 can coordinately activate the transcription factor MYB4,35 thus further repressing the expression of CHS, which then inhibits the biosynthesis of flavonoids and promotes more phenylalanine directed to lignin synthesis.36 The expression of TRINITY DN87586_c5_g1 (CpMYB20) in C. paliurus was low at the F3 stage. Therefore, we can hypothesize that the accumulation of flavonoids during this period may be related to the low expression of MYB20, resulting in a failure to activate the MYB4 transcription factor, which thus inhibits flavonoid biosynthesis. In addition, MYB111 (TRINITY DN94784_c0_g4) and AtMYB111 were clustered together. In Arabidopsis, AtMYB11, AtMYB22, and AtMYB111 jointly activated CHS, CHI, F3H, and FLS and promoted the accumulation of large amounts of flavonols.37 TRINITY DN94784_c0_g4 (CpMYB111) in C. paliurus was highly expressed at the F1 stage, with a similar expression trend and subsequent accumulation of a large number of flavonols at the F1 stage. TRINITY DN83924_c1_g6 (CpMYB1) and AtMYB1 were clustered into one group. In onions,38 apples,39 and purple sweet potatoes,40 MYB1 is positively correlated with anthocyanin synthesis. TRINITY DN83924_c1_g6 (CpMYB1) is highly expressed at the F1 stage, where most cyanidin compounds accumulate. CpMYB44 (TRINITY DN90627_c1_g1) and AtMYB44 were also clustered into one group. MYB44 is a special transcription factor involved in the catechin biosynthesis pathway during the blooming process of Camellia and is positively correlated with the accumulation of catechins.41 TRINITY DN90627_c1_g1 (CpMYB44) is highly expressed at the F3 and F4 stages, resulting in catechins and their derivatives accumulating in large amounts during the F4 stage. TRINITY DN85072_c1_g3 (CpbHLH96) and AtbHLH96 were clustered into a group, while TRINITY DN90632_c1_g1 (CpbHLH66) and AtbHLH66 were clustered into another group. The cis-acting element analysis of genes related to the synthesis of flavonols and anthocyanins in grapes revealed that most gene promoters contain bHLH elements.42 TRINITY DN85072_c1_g3 (bHLH96) and TRINITY DN90632_c1_g1 (bHLH66) had higher expression levels in the F4 period. These two genes may induce related flavonols and anthocyanins to be expressed in large amounts in C. paliurus leaves during the F4 period. The determination of transcription factors involved in flavonoid biosynthesis in C. paliurus requires more gene-level experiments to verify their specific roles.

RT-qPCR Verification

We selected eight genes that are closely related to the synthesis of flavonoids in C. paliurus for real-time quantitative polymerase chain reaction (RT-qPCR) verification. These eight genes have high expression levels and are differentially expressed across the four different developmental stages. These verification results are consistent with the expression trend of the sequencing results (Figure 10), which demonstrates that the transcriptome data are reliable.

Figure 10.

Figure 10

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RT-qPCR results. The left ordinate represents the value obtained by fluorescence quantitative analysis of differentially expressed genes, which is presented in the form of a bar chart. The right ordinate represents the expression value of the corresponding structural genes in this study, presented in the form of a line graph. r represents the correlation between the two data sets.

Conclusions

During the development of C. paliurus leaves, the total flavonoid content increased first and reached its peak at the F3 stage, with a decrease occurring at the F4 stage. A total of 137 differential flavonoids among four different developmental stages were identified in this study. Combined transcriptome and metabonomic data analysis for C. paliurus leaves at four different developmental stages identified seven key flavonoid synthesis enzymes (PAL, C4H, CHS, FLS, DFR, I2′H, UGT72E) and six transcription factors (MYB1, MYB20, MYB44, MYB111, bHLH66, bHLH96) likely involved in the biosynthesis of flavonoids in C. paliurus. Thus, the flavonoid biosynthesis pathway in the leaves of C. paliurus was examined. These results provide new data for future research on the biosynthesis of flavonoids in C. paliurus as well as a guidance for the collection of C. paliurus leaves with higher flavonoid contents.

Experimental Section

Plant Samples

The leaf samples of C. paliurus used in this experiment were collected from Zhuzhang Village, Longquan City, Lishui City, Zhejiang Province, China (E118°48′28″, N28°5′57″) in early May 2018. According to their leaf areas, we divided the collected leaves into four different development stages: the smallest fully expanded leaves (F1 stage), small leaves (F2 stage), intermediate-sized leaves (F3 stage), and the largest fully expanded leaves (F4 stage) (Figure 1).43 The leaves were stored in a liquid nitrogen tank immediately after being collected from the branches. After returning to the laboratory, the leaves were transferred to a −80 °C freezer for storage. The leaves were ground into powder by adding liquid nitrogen. Three biological replicates were selected from each group for the next step of the transcriptional metabolism analysis.

Determination of the Total Flavonoid Content

The total flavonoids in C. paliurus leaves were extracted by ultrasonic-assisted extraction. In brief, 1 g of C. paliurus powder was dried and put into a 25 mL volumetric flask. The volume was fixed with 70% ethanol and ultrasonicated at 70 °C for 60 min. After that, centrifugation was performed at 4000 rpm for 20 min. Then, 1 mL of supernatant was transferred into a 25 mL volumetric flask, and the volume was fixed with 70% ethanol.

The content of total flavonoids was measured by AlCl3 colorimetry. First, 2 mL of a diluent was transferred into a 10 mL volumetric flask to which 0.6 mL of 5% NaNO2 was added. The solution was allowed to stand at room temperature for 5 min, after which 0.6 mL of 10% AlCl3·6H2O was added. Again, the solutions were allowed to stand at room temperature for 5 min, after which an additional 4 mL of 1 M NaOH was added. Finally, the solution was allowed to stand at room temperature for 15 min, after which the volume was fixed with 70% ethanol. The total flavonoid content was calculated using rutin as a standard.44 All extractions and determinations were carried out for three replicates. Statistical analyses were performed with SPSS software package (version 17.0).45 The data were graphically plotted with Origin Pro software (version 8.0).46,47

Metabolic Analysis

Samples were crushed for 1.5 min at 30 Hz using a grinder (MM400, Retsch, Haan, Germany) before metabolic analysis. First, 0.1 g of the samples was weighed and dissolved in 70% ethanol solution. The samples were stored in a refrigerator at 4 °C overnight. To improve the extraction rate, we conducted three vortex treatments during this period. The samples were centrifuged at a speed of 10,000g for 10 min, and the supernatant was obtained by discarding the precipitate. The supernatant was filtered through a microporous membrane (SCAA-104, 0.22 μm pore size, ANPEL, Shanghai, China, http://www.anpel.com.cn/). The samples were stored in sample bottles (CNWBOND Carbon-GCBSPE Cartridge, 250 mg, 3 mL, ANPEL, Shanghai, China, www.anpel.com.cn/cnw) prior to analysis.

The metabolite data acquisition system is composed of a tandem mass spectrometer (MS/MS, Applied Biosystems 6500 QTRAP, Applied Biosystems, Shanghai, China) and an ultra-performance liquid chromatograph (UPLC, Shim-pack UFLC SHIMADZU CBM30A, Shimadzu Corp., Beijing, China). Mass spectrometry conditions were set as follows: 5500 V mass spectrometry voltage, 25 PSI curtain gas (CUR), and electrospray ionization at 500 °C. Collision-activated dissociation was set to a “high” parameter. The bases for scanning each off-pair in the triple quadruple rod (QQQ) were collision energy and optimized declustering potential.48 The following ultra-high performance liquid chromatography conditions were used: a Waters ACQUITY UPLC HSS T3 C18 1.8 m, 2.1 mm × 100 mm column was used for chromatographic analysis, muconitrile was the organic phase, and ultrapure water was the aqueous phase, the elution gradient was a muconitrile/water solution of 5:95 (V/V) at 0 min, 95:5 (V/V) at 11 min, 95:5 (V/V) at 12 min, 5:95 (V/V) at 12.1 min, 5:95 (V/V) at 15 min, 2 μL injection, the column temperature was set to 40 °C, and the flow rate was controlled at 0.4 mL/min.

A qualitative analysis of metabolites was performed by comparing the secondary spectrum information with public databases of metabolite information. Interferences from NH+ ionization, Na+ ionization, K+ ionization repetitive signals, isotope signals, and fragment ion repetitive signals were removed during the analysis. A quantitative analysis was accomplished by MRM with triple quadruple-rod mass spectrometry. After mass spectrometry analysis of 12 samples at four different developmental stages, the peak areas of all metabolites were integrated, and the same metabolites between different samples were corrected.49

There were three biological replicates in each group of samples of C. paliurus at four different developmental stages: F1, F2, F3, and F4. During the screening process of differential metabolites, we selected the metabolites with VIP ≥ 1 (the VIP value can represent the influence of a specific metabolite group difference in classification, and metabolites with VIP ≥1 are generally considered to have significant differences). Differences were also screened based on the multiple of metabolite differences. Metabolites with a fold change ≤ 0.5 or a fold change ≥ 2 were determined to have significant differences between developmental stages of C. paliurus.

Principal component analyses of the different compositions of flavonoids in the leaves of C. paliurus at four different developmental stages were conducted using the R statistical computing environment (Version 3.5.0).

Transcriptome Analysis

Total RNA was extracted from the leaves of C. paliurus at different developmental stages using a total RNA extractor (Trizol) Extraction Kit (B51311, Sangon Biotechnology, Shanghai, China). There were three biological replicates for each group, with a total of 12 samples. The Qubit2.0 kit (Q32855, Life, Shanghai, China) was used to detect the RNA concentration. Agarose gel electrophoresis was used to detect the genomic contamination and the RNA integrity.

The mRNA library constructed was conducted with the VAHTS mRNA-seq V2 Library Prep Kit for Illumina (NR601-02, Vazyme Biotechnology, Nanjing, China), after which the total RNA was quantified accurately by using the Qubit2.0 RNA detection kit (Q32855, Life, Shanghai, China). The first and second strand cDNAs were synthesized using the T100TM thermal cycler (Bio-Rad, Hercules, CA, USA). The double-stranded cDNAs were purified by adding VAHTS DNA Clean Beads. The remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. Purification of the ligation product was conducted with VAHTS DNA Clean Beads. First, the magnetic beads were dissolved in nuclease free water and then transferred to a new nucleic-acid-free centrifuge tube. In the last step of library amplification, the PCR conditions were as follows: (1) 98 °C for 30 s, (2) 15 cycles of 98 °C for 10 s, 60 °C for 30 s, and 72 °C for 30 s, (3) 72 °C for 5 min, and (4) 4 °C thereafter. The quality of the library was assessed on the Bioanalyzer 2100 system (Agilent Technologies Inc., Santa Clara, CA, USA).

After quantification and pooling, paired-end sequencing of these libraries was performed on HiSeq XTen sequencers (Illumina, San Diego, CA, USA) by Novogen Co., Ltd. (Beijing, China). FastQC was used to evaluate the quality of the raw data, and Trimmomatic (version 0.36) was used to obtain clean read data by quality trimming and adapter clipping. Trinity (version 2.0.6) (parameter: min_kmer_cov 2) (Trinity Technologies, Irvine, CA, USA) was used for de novo assembly of the clean reads from the obtained samples. The transcripts were then compiled, and gene annotation was conducted using the NCBI Nr (NCBI non-redundant protein database), TrEMBL, CDD (Conserved Domain Database), Swiss-Prot, KOG (eukaryotic Orthologous Groups), NR, COG (Cluster of Orthologous Groups), PFAM (Protein families), and NT (Nucleotide Sequence) databases. The Gene Ontology (GO) functional annotation was obtained by comparing the transcripts with the Swiss-Prot and TrEMBL databases, and transcript Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation information relied on KAAS acquisition. The RNA-Seq data set is available in the National Center for Biotechnology Information (NCBI) database (accession no. PRJNA 548403).

Gene expression was calculated by Salmon, and the gene expression difference was visualized by DESeq2. The screening condition for identifying the differential expression was defined as transcripts with a q-value ≤ 0.05 and a |fold change| ≥ 2. Based on the analysis results, a heat map was drawn using Tbtools software for cluster analysis.

RT-qPCR Validation

The pre-extracted RNA was reverse-transcribed into cDNA using a HiScript II Reverse Transcriptase-based two-step qPCR kit (Vazyme Biotech Co. Ltd., Nanjing, China). Eight different genes involved in flavonoid biosynthesis were selected for validation. Primer5.0 software was used for primer design, and beta-Actin-1 was selected as the internal reference gene.50 Three technical replicates were used for each gene involved in the validation. Three biological replicates were also used for each group of four samples at different developmental stages. The amplification system was constructed using ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co. Ltd., Nanjing, China) and placed in CFX Connect (Bio-Rad Laboratories Inc. Hercules, CA, USA) for real-time fluorescence quantitative PCR. The relative expression of genes was calculated using the 2–ΔΔCt method. The Corrplot package in R-3.6.1 was used for correlation analysis to verify the credibility of the transcriptome data analysis results. The data were graphically plotted with Origin Pro software (version 8.0).46,47

Prediction of Key Transcription Factors and Co-Expression Network Analysis

PlantTFDB was used to predict the key transcription factors involved in flavonoid biosynthesis. Log2-transformation was performed on the expression data from differentially expressed transcription factors, other differentially expressed genes, and differentially accumulated metabolites before data analysis. The Corrplot package in R-3.6.1 was used to calculate the correlation between differentially expressed transcription factors and other differentially expressed genes as well as differentially expressed genes and differentially accumulated metabolites. A visual network graph was generated with Cytoscape software (version 3.6.1).

Acknowledgments

This work was financially supported by the Zhejiang Provincial Key Research and Development Program (2018C02021) and the Ten Thousand Talent Program of Zhejiang Province (2019R52043).

Glossary

Abbreviations

PAL

phenylalanineammonia-lyase

4CL

4-coumarate-CoA ligase

FNSI

flavone synthase I

C4H

trans-cinnamate 4-monooxygenase

CHI

chalcone isomerase

I2′H

4′-methoxyisoflavone 2′-hydroxylase

F3H

naringenin 3-dioxygenase

F3′H

flavonoid 3′-monooxygenase

F3′5′H

flavonoid-3′,5′-hydoxylase

FLS

flavonol synthase

DFR

flavanone 4-reductase

LAR

leucoanthocyanidin reductase

ANS

anthocyanidin synthase

ANR

anthocyanidin reductase

mal

malonyl

O-h

O-hexoside

g

glucoside

h-O

hexosyl-O

r

rutinoside

ace

acetyl

met

methyl

fer

feruloy

p

pentoside

sin

sinapoyl

glua

gluconic acid

eude

eudesmic acid

mal

malonyl

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.1c00059.

  • Flavonoids in C. paliurus, different flavonoids in C. paliurus, correlation of differential flavonoid synthesis genes and differential metabolites in C. paliurus, and correlation between differential flavonoid synthesis genes and differential transcription factors in C. paliurus (PDF)

The authors declare no competing financial interest.

Supplementary Material

ao1c00059_si_001.pdf (178.3KB, pdf)

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