ABSTRACT
There is much variation in the distributions of secondary metabolites, such as flavonoids, among plant tissues. The flavonoids in Chinese jujube (Ziziphus jujuba Mill.) and sour jujube (Ziziphus spinosa Hu.) provide various health benefits. Variation in the distributions of flavonoids among the tissues of jujube and sour jujube has not yet been characterized. A total of 101 flavonoid metabolites, including 30 flavones, 21 flavonols, 11 flavanones, 11 isoflavanones, 8 flavanonols, 7 chalcones, 4 flavanols, 3 flavone glycosides, 2 phenolic acids, 2 ×anthones and 2 unclassified flavonoids, were identified in the fruits, leaves, flowers and seeds of Z. jujuba cv. Dongzao (DZ) and Z. spinosa cv. Liyuanzhenzhu8 (LYZZ8) using UPLC-ESI MS/MS. Principal component analysis and hierarchical cluster analysis revealed that flavonoids from the same tissues of DZ and LYZZ8 were clustered, with the exception of those from the leaves. Tissue-specific flavonoid metabolites in different tissues of DZ and LYZZ8 were identified. The main flavonoids varied among tissues, the total flavonoid content and the content of most individual flavonoids in all tissues of DZ and LYZZ8 varied (p < .05). Furthermore, 40, 50, 49 and 47 differential flavonoid metabolites were detected in the fruits, leaves, flowers and seeds of DZ and LYZZ8, respectively. Potential chemical markers for differentiating among tissues in DZ and LYZZ8 as well as among the same tissues of DZ and LYZZ8 were identified. Overall, our findings provided new insights into the flavonoid of jujube and will aid the utilization of jujube and sour jujube.
Introduction
Chinese jujube (Ziziphus Jujuba Mill.), is an economically important seasonal fruit,[Citation1] with a cultivation history in China of approximately 7,000 years,[Citation2] it thus makes a major contribution to the income of farmers in China.[Citation3] The tissues (e.g., seed, fruit, and leaf) of Chinese jujube contain various nutrients, including amino acids, nucleotides, vitamin C, polysaccharides, flavonoids, phenolic acids, triterpene acids, saponins, and alkaloids.[Citation4] In addition, the nutrients in jujube tissues have been shown to have diverse biological activities, such as antioxidant,[Citation5,Citation6] antiallergic,[Citation7] anti-inflammatory,[Citation8,Citation9] antitumor,[Citation10] antidiabetic,[Citation11] and hepatoprotective[Citation12] activities.
Sour jujube (Ziziphus Spinosa Hu.), a perennial shrub that is considered the feral father species of Chinese jujube,[Citation13] is native to China; it primarily occurs in northern China and a few southern provinces.[Citation14] In East Asia, it has been consumed or utilized as a traditional remedy for thousands of years.[Citation15] Additionally, sour jujube is ecologically and economically important for the greening of infertile land and providing wind control and sand fixation services in desert.[Citation16] The seeds[Citation17] of sour jujube are widely utilized in pharmaceutical industries for their sedative and hypnotic properties. The fruits of jujube and sour jujube can be consumed fresh or dried[Citation18] and applied as food additives[Citation19] to enhance flavor[Citation20]; the leaves of jujube and sour jujube can be used to make tea[Citation21,Citation22]; and the seeds of jujube and sour jujube can be used as raw materials for the development of functional products.[Citation23] Jujube and sour jujube are the most widely used and valuable species in jujube genus.[Citation24,Citation25]
Flavonoids are a large class of polyphenolic chemicals that are widely present in plant tissues[Citation26] including the leaves, flowers, and seeds.[Citation27] Flavonoids are abundant in plant tissues; they are thus major sources of flavonoids. Flavonoids are closely related to plant biological functions including pollen fertility, signaling, auxin transport regulation, pigmentation, and resistance,[Citation28] and they play key roles in the human body. Several studies have been conducted to identify flavonoids and their content in various tissues of jujube and sour jujube.[Citation29–31] Most previous studies have focused on determining the total content of flavonoids in various tissues in jujube and sour jujube; however, studies that compare the composition or content of flavonoids among tissues, jujube varieties, or various developmental stages are rare by comparison.
The bioactive components in traditional Chinese medicines, which are primarily secondary metabolites in various tissues, are closely related to their quality.[Citation32,Citation33] Secondary metabolites are distributed differently in different plant tissues.[Citation34] Previous studies of the bioactive components in jujube have aimed at identifying chemical markers that could be used to distinguish between fruits of jujube and sour jujube,[Citation35] leaves of jujube and sour jujube,[Citation36] seeds between sour jujube and Indian jujube[Citation37–39] (Ziziphus mauritiana Lam.) and jujube geographical characteristics.[Citation40] However, previous analyses have been conducted using a single tissue of two jujube species, thus, chemical markers for distinguishing between tissues of jujube and sour jujube have not yet been identified. Additional research is needed to clarify the composition and content of flavonoids in the fruits, leaves, flowers, and seeds of jujube and sour jujube, as well as identify chemical markers in these plants in various tissues.
Metabolomics is an effective and powerful approach for conducting qualitative and quantitative analyses of differential metabolites and crucial biomarkers among samples.[Citation41] Various metabolomics studies of jujube have been conducted.[Citation42–44] To our knowledge, studies of the distribution of flavonoid metabolites in various tissues of sour jujube and jujube and differences in flavonoid metabolites among the same tissues of jujube and sour jujube have not yet been conducted. Our findings provide insights into the flavonoid of jujube and sour jujube and implications for their utilization.
Materials and methods
Plant materials
Samples of the jujube (Ziziphus jujuba Mill.) variety “Dongzao” and sour jujube (Ziziphus spinosa Hu.) variety “Liyuan zhenzhu8” were collected from the Daming Jujube Germplasm Repository (36°18′N, 115°09′E, 100 m above sea level), which is located in Daming County, Handan City, Hebei Province, China. Twelve healthy jujube and sour jujube plants with similar status were sampled; three biological replicates were collected, with two samples comprising a single biological replicate. Samples in a single biological replicate were pooled after they were collected. Jujube and sour jujube flowers were collected in the full-blossom period, healthy jujube and sour jujube fruits of equal size and ripeness and free of mechanical injury were collected during the semi-red period; leaves of jujube and sour jujube were also collected. The flowers and leaves were wrapped with aluminum foil and immediately placed in liquid nitrogen. The sarcocarps (including peel and flesh) and the seeds (protected by the shell) were separated using a scalpel. The sarcocarps were diced, and the intact seeds were obtained by removing the shell thoroughly; they were then wrapped with aluminum foil and placed in liquid nitrogen. All samples were sent back to the lab instantly, where they were maintained in a freezer at −80°C until they were used. The abbreviations for the different tissues of “Dongzao” and “Liyuan zhenzhu8” are listed in .
Table 1. The abbreviation of different tissues of “Dongzao” and “Liyuan zhenzhu8.
Chemicals and reagents
Acetonitrile and methanol (MeOH) of HPLC quality were obtained from Merk (Darmstadt, Germany). All tests were conducted using MilliQ water (Millipore, Bradford, USA). Formic acid was obtained from Sigma-Aldrich. All the standards were obtained from MCE (MedChemExpress, China). The stock solutions of the standards were created at a concentration of 10 mmol/L in 70% MeOH. All stock solutions were maintained at a temperature of −20°C. To make working solutions for analysis, 70% MeOH was used to dilute the stock solution.
Sample preparation and extraction
Samples were freeze-dried, crushed into powder (30 Hz, 1.5 min), and stored at −80°C. Next, 0.5 ml of 70% methanol was used to extract 20 mg of powder. Ten μL of internal standards, including [2H6] daidzein and [2H3] rutin (4,000 mmol/L), was mixed with the extract. The extract was centrifuged at 12,000 g under 4°C for 5 min after being sonicated for 30 min. For subsequent LC-MS/MS analysis, the supernatant was filtered through a 0.22 μm membrane filter.
Chromatographic condition
An UPLC-ESI-MS/MS system (UPLC, ExionLC™ AD, https://sciex.com.cn/; MS, Applied Biosystems 6500 Triple Quadrupole, https://sciex.com.cn//) was used to analyze the sample extracts. UPLC was conducted using a Waters Acquity UPLC HSS T3 C18 (100 mm × 2.1 mm i.d.,1.8 µm) column with a solvent system of water with 0.05% formic acid (A) and acetonitrile with 0.05% formic acid (B). The gradient elution program was as follows: 0–1 min, 10–20% B; 1–9 min, 20–70% B; 9–12.5 min, 70–95% B; 12.5–13.5 min, 95% B; 13.5–13.6 min, 95–10% B; and 13.6–15 min, 10% B; The flow rate and temperature were set at 0.35 mL/min and 40°C, respectively. The injection volume was 2 μL.
ESI-MS/MS Conditions
A triple quadrupole-linear ion trap mass spectrometer (QTRAP), which operates in both positive and negative ion mode and is managed by Analyst 1.6.3 software (Sciex), was used to acquire the Linear ion trap (LIT) and trip quadrupole (QQQ) scans. The ESI source operation settings were as follows: ESI±, 550°C, 5,500 V (positive), and −4,500 V (negative) for the ion spray voltage and 35 psi for the curtain gas. Flavonoids were examined using multiple reaction monitoring (MRM). Data were obtained using software from Analyst 1.6.3. The Multiquant 3.0.3 program (Sciex) was used to calculate the quantity of each metabolite. Further refinement of the declustering potentials (DPs) and collision energies (CEs) was conducted to determine the mass spectrometer’s characteristics for each particular MRM transition. According to the metabolites eluted at each interval, a particular set of MRM transitions was observed.
Qualitative and quantitative flavonoid metabolites analysis
The qualitative analysis of flavonoid metabolites was conducted based on the retention time of each flavonoid metabolite. The quantitative analysis was performed using the QQQ scans in MRM mode. QQQ first screens the parent ion (Q1) of the target substance and excludes the corresponding ions of other substances with different molecular weights to preliminarily exclude the interference. The parent ions were induced by the collision chamber and fractured to form multiple fragment ions. The fragment ions were filtered through the QQQ; the desired characteristic fragment ions were selected, and the interference of non-targeted ions was excluded. After the mass spectrometry data of different samples were obtained, the chromatographic peaks of all targets were integrated and quantified by standard curves.
Establishment of the standard curves
The internal standard method can generate more accurate results than the external standard method, especially for the establishment of standard curves. However, identifying a proper internal standard remains a challenge. In this study, only the isotope of daidzein and rutin, [2H6] daidzein and [2H3] rutin, were detected. At the peak of the rutin standard curve, the internal standard response was suppressed; given that this would affect the linearity of the standard curve, rutin was excluded. [2H3] Rutin was added to the extraction of samples to characterize the stability of the sample pretreatment. The rest of the flavonoid standard curves were established using the external standard method. Information on all standards used in this UPLC-ESI-MS/MS system is listed in Table S1.
Different concentration of standard solutions including 0.5 nmol/L, 1 nmol/L, 5 nmol/L, 10 nmol/L, 20 nmol/L, 50 nmol/L, 100 nmol/L, 200 nmol/L, 500 nmol/L, 1000 nmol/L, 2000 nmol/L, were prepared, and the chromatographic peak intensity data of corresponding quantitative signals of each concentration of standard solutions were obtained. All 101 flavonoid standard curves were established; the concentration ratio or concentration is the abscissa and the area ratioor area or area is the vertical ordinate. The results of standard curves are listed in Table S2.
Flavonoid content calculation
All flavonoid metabolites were quantified using the external standard method, with the exception of daidzein. Data on the content of flavonoids in the actual samples were generated by substituting the synthetic peak area of each flavonoid identified in the samples into the linear equations of the standard curves.
Finally, the individual flavonoids content was calculated by multiplying the amount of substance of each flavonoid metabolite by their corresponding molecular weight. The formula for calculating the amount of a flavonoid in a substance is n (nmol/g) = A*B/1000000/M, where A represents the concentration (nmol/g) calculated by replacing the integrated peak area of a flavonoid in the sample alongside the corresponding standard curve, B represents the volume of extraction-related solution utilized (μL), and M represents the weighted sample’s mass (g). The unit for the flavonoid content used in this study was µg/100 g.
Quality control (QC)
Analyst 1.6.3 software was used to handle the mass spectral data. Figure S1(a) depicts a representative overlapping TIC (total ions current) about QC sample. Each distinct hue of peak in the TIC picture corresponds to a discovered flavonoids metabolite. Figure S1(b) displays a multi-peak metabolite identification plot produced by MRM mode.
Screening criteria
The thresholds of screening differential flavonoid metabolites were as follows, one is VIP (generated by OPLS-DA model) ≥ 1 coupled with FC (fold change) ≥ 1.5 and ≤ 0.67 (two groups), the other is VIP (generated by PLS-DA model) ≥ 1 combined with p value (non-parametric Krustal Wallis test, multiple comparisons).
Data processing and visualization
All samples used in this study were triply replicated. ANOVA, Krustal Wallis test, PCA, HCA, PLS-DA and OPLS-DA were all performed using R v.4.2.3 (https://www.r-project.org/) software. Multiple comparisons of Krustal Wallis test were conducted in agricolae R package, p adjustment was corrected by Benjamini and Hochberg method. PLS-DA and OPLS-DA were carried out using the ropls[Citation45] R package. Upset plot and heatmap were drawn under assistance of UpSetR and pheatmap R packages, respectively. Volcano plot, S-plot and PCA plot were all done with the aid of ggplot2 R package.
Results
Comparative analysis of DZ and LYZZ8 at the whole metabolome scale
The composition and content of flavonoid metabolites in the fruits, leaves, flowers, and seeds of jujube and sour jujube were investigated using absolute quantitative flavonoid metabolomics. A total of 101 flavonoid metabolites, including 30 flavones, 21 flavonols, 11 flavanones, 11 isoflavanones, 8 flavanonols, 7 chalcones, 4 flavanols, 3 flavone glycosides, 2 phenolic acids, 2 ×anthones, and 2 unclassified flavonoids were identified (; ). Significant differences were observed in the content of most individual flavonoid metabolites in different tissues of DZ and LYZZ8 (p < .05).
Figure 1. Number and proportion of each class of flavonoids detected in this study.
Table 2. The content of flavonoids detected in different tissues of “Dongzao” and “Liyuan zhenzhu8”.
To further explore the diversity of flavonoid metabolites among tissues of DZ and LYZZ8, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to analyze flavonoid metabolite profiles. According to the PCA results, PC1 and PC2, which were the two main principal components, explained 38.84% and 20.87% of the total variance in the eight samples, respectively. The fruit, flower, and seed samples of DZ and LYZZ8 were clustered, whereas the leaf samples of DZ and LYZZ8 were separated (). These same results were observed in the HCA (), which indicated that the patterns of flavonoid accumulation in the leaves of DZ and LYZZ8 differed, whereas those in the fruits, flowers and seeds in DZ and LYZZ were similar.
Figure 2. Principal component analysis (PCA) plot of the flavonoid metabolic profiles in different tissues of “Dongzao” and “Liyuan zhenzhu8.
Figure 3. Heat map based on the contents of all flavonoids detected in this study. The content was the mean value of triplicate determinations. The data was standardized, and hierarchical cluster analysis was performed. Each column represents one tissue, and each row represents one flavonoid. Red. indicates high-content flavonoids, whereas green indicates low-content flavonoids.
Analysis of tissue-specific flavonoid metabolites in DZ and LYZZ8
To reveal the number of flavonoid metabolites in different tissues of DZ and LYZZ8, an UpSet plot was made (); the number of flavonoid metabolites of flower, seed, leaf and fruit of DZ was 73, 62, 55 and 49, respectively, and the number of flavonoid metabolites in the flower, seed, leaf, and fruit of LYZZ8 was 69, 60, 53, and 50, respectively. The number of flavonoids in the same tissues of DZ and LYZZ8 was nearly the same; however, the number of flavonoid metabolites in different tissues of DZ and LYZZ8 differed. Flavonoids were most abundant in the flowers, followed by the seeds, leaves and fruits. In addition, 25 flavonoid metabolites were present in all eight samples. The numbers of flavonoid metabolites present in single tissues were as follows: 3, 2, 1, and 1 for flower, leaf, fruit, and seed in DZ, respectively; 3, 1, 1, and 1 for flower, leaf, fruit, and seed in LYZZ8, respectively (). In general, there were differences in the numbers of flavonoid metabolites in different tissues of DZ and LYZZ8; tissue-specific flavonoid metabolites were identified to provide insights into the special uses of different tissues.
Figure 4. UpSet plot showing the flavonoids shared among and specific to different tissues of “Dongzao” and “Liyuan zhenzhu8.
Table 3. Tissue-specific flavonoid metabolites in “Dongzao” and “Liyuan zhenzhu8”.
Analysis of the distribution of flavonoid metabolites and the main flavonoid metabolites in different tissues of DZ and LYZZ8
To clarify the distribution of different types of flavonoids in various tissues of DZ and LYZZ8, we conducted an analysis of the total flavonoid content and the content of each type of flavonoid. Significant differences were observed in the total flavonoid content of the eight samples (p < .01) (; ). The flavonoid content was highest in ZJS, followed by ZJL and ZSB, and the flavonoid content was lowest in ZSL. Flavanols were the most common flavonoids in the fruits of DZ and LYZZ8, which accounted for over 78% of the total flavonoids in ZJF and ZSF, although only four individual flavanols were detected. Flavonols, flavones, and chalcones were the main flavonoids in the leaves of DZ and LYZZ8, and they comprised up to 88% of the total flavonoid content of ZJL and ZSL. Flavonols, flavones, chalcones, and flavanones were the major flavonoids in the flowers of DZ and LYZZ8, and they comprised up to 91.6% of the total flavonoid content of ZJB and ZSB. Flavone glycosides were the main flavonoids in the seeds of DZ and LYZZ8, and they comprised 90% of the total flavonoids of ZJS and ZSS.
Figure 5. The distribution of total contents of 11 flavonoid subclasses in different tissues of “Dongzao” and “Liyuan zhenzhu8” (p < .01).
Figure 6. The proportion of each flavonoid subclass in “Dongzao” and “Liyuan zhenzhu8.
To further elucidate variation in the content of individual flavonoid metabolites in different tissues of DZ and LYZZ8, we analyzed the 20 most common flavonoid metabolites. The content of the 20 common flavonoid metabolites differed significantly among tissues of DZ and LYZZ8 (p < .05) (). In ZJF, the main individual flavonoids were (-)-epicatechin (56.33%), (-)-catechin (27.31%), and rutin (14.37%), which was in consistent with the results of Xue[Citation46]; in ZSF, (-)-catechin (40.37%), epicatechin (38.51%), and rutin (16.61%) were the main individual flavonoids. In ZJL, quercitrin (27.15%), nicotiflorin (22.37%), rutin (19.75%), and phlorizin (15.65%) were the major individual flavonoids; in ZSL, nicotiflorin (37.63%), rutin (33.50%), phlorizin (9.79%), and quercitrin (9.25%) were the major individual flavonoids. In ZJS, spinosin (93.61%), nicotiflorin (2.51%), and rutin (1.27%) were the main individual flavonoids, which was consistent with the findings of Choi[Citation29]; in ZSS, spinosin (88.37%), nicotiflorin (6.89%) and rutin (1.15%) were also the main individual flavonoids, which was consistent with the findings of Bao.[Citation47] In ZJB, rutin (21.66%), nicotiflorin (13.45%), quercitrin (11.74%) and quercetin (11.68%) were the four most abundant flavonoids; quercitrin (21.34%), rutin (14.61%), hesperidin (11.63%) and phlorizin (11.48%) were the four most abundant flavonoids in ZSB. The main individual flavonoids in ZJB and ZSB differed, and this was not consistent with the patterns observed in the other three tissues.
Table 4. Content of the 20 most abundant flavonoids in different tissues of “Dongzao” and “Liyuan zhenzhu8”.
Levels of epicatechin and (-)-catechin were substantially higher in ZJF and ZSF than in other tissues, and levels of spinosin were significantly higher in ZJS and ZSS than in other tissues; the content of nicotiflorin and rutin was higher in ZJL and ZSL and the content of phlorizin was higher in ZJL than in other tissues. The content of quercetin, hesperidin, hydroxysafflor yellow A, kaempferol, phloretin, diosmin, hyperoside, eriodictyol, and tiliroside was higher in ZJB and ZSB than in other tissues.
Identification of chemical markers for distinguishing among tissues of DZ and LYZZ8
Supervised multivariate statistical analysis methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) are more effective than PCA for characterizing differences in metabolites among groups of samples.[Citation48] Variable importance in projection (VIP) indicates the variable weight values of the (O) PLS-DA model and can be used to assess the degree to which variation in metabolite accumulation affects the differentiation of samples within each group as well as the magnitude of its effect.[Citation49] Differential metabolites were identified using the following criterion: VIP > 1. The PLS-DA method is considered effective for multi-classification, and the OPLS-DA approach is less prone to model overfitting for binary classification.[Citation50]
The PLS-DA approach was used to identify differential flavonoid metabolites among tissues of DZ and LYZZ8. The DZ and LYZZ8 PLS-DA score and loading plots are shown in . The values of DZ and LYZZ8 PLS-DA model parameters, including R2X, R2Y, and Q2, were 0.99, 0.99, and 0.99, respectively, and the values of these parameters suggested that the fit and predictive capacity of both models were high. Markers were screened based on the loading plots, as well as the following two criteria: VIP ≥ 1 and p-value <.05. indicated that the four tissues of DZ and LYZZ8 were divided into four groups; in the PLS-DA loading plots of DZ and LYZZ8, the labeled flavonoids were located far away from the origin points and other flavonoids (). The VIP values from the PLS-DA models of DZ and LYZZ8 were used for further screening, and flavonoids with VIP ≥ 1 were obtained. The p-values of the non-parametric tests of differences in the flavonoid content were determined for the labeled flavonoids and flavonoids with VIP ≥ 1 to identify potential chemical markers. Eight potential markers identified for distinguishing tissues of DZ were as follows: spinosin, (-)-epicatechin, (-)catechin, nicotiflorin, quercetin, rutin, phlorizin, and quercitrin; the 10 potential markers for LYZZ8 were spinosin, (-)-epicatechin, (-)-catechin, nicotiflorin, quercetin, hesperidin, rutin, kaempferol, phlorizin, and quercitrin. The results are shown in .
Figure 7. The PLS-DA scores of different tissues in “Dongzao.
Figure 8. The loading scatter plot of flavonoid metabolites in different tissues in “Dongzao”.
Figure 9. The PLS-DA scores of different tissues in ‘Liyuan zhenzhu8.
Figure 10. The loading scatter plot of different scores of different tissues in ‘Liyuan zhenzhu8’tissues in “Liyuan zhenzhu8.
Table 5. Chemical markers for distinguishing among tissues of “Dongzao”.
Table 6. Chemical markers for distinguishing among tissues of “Liyuan zhenzhu8”.
Analysis of differential flavonoid metabolites in the same tissues of DZ and LYZZ8
OPLS-DA approach was used to identify differential flavonoid metabolites in the same tissues of DZ and LYZZ8. The models were established for comparisons of the fruits of DZ and LYZZ8 (ZJF_VS_ZSF), leaves of DZ and LYZZ8 (ZJL_VS_ZSL), flowers of DZ and LYZZ8 (ZJB_VS_ZSB), and seeds of DZ and LYZZ8 (ZJB_VS_ZSB). The criteria for screening differential flavonoid metabolites were as follows: VIP ≥ 1 coupled with fold-change (FC) ≥ 1.5 (up-regulated) and FC ≤ 0.67 (down-regulated). FC was calculated using the latter mean value divided by the former mean value.
In ZJF_VS_ZSF, 40 differential flavonoid metabolites were identified, including 12 flavonols, 8 flavones, 6 flavanones, 5 chalcones, 4 isoflavanones, 3 flavanonols, 1 flavone glycoside, and 1 flavanol (Figure S2, Table S3); in ZJL_VS_ZSL, 50 differential flavonoid metabolites were identified, including 15 flavonols, 11 flavones, 5 flavanones, 5 flavanonols, 4 isoflavanones, 3 chalcones, 3 flavanols, 2 flavone glycosides, 1 phenolic acid, and 1 unclassified flavonoid (Figure S3, Table S4); in ZJB_VS_ZSB, 49 differential flavonoid metabolites were identified, including 15 flavones, 7 flavanonols, 7 flavonols, 5 flavanones, 4 chalcones, 4 isoflavanones, 3 flavanols, 2 phenolic acids, 1 flavone glycoside, and 1 ×anthone (Figure S4, Table S5); and in ZJS_VS_ZSS, 47 differential flavonoid metabolites were identified, including 14 flavones, 8 flavonols, 6 flavanones, 5 isoflavanones, 4 chalcones, 4 flavanonols, 3 flavanols, 1 flavone glycoside, 1 phenolic acid, and 1 unclassified flavonoid (Figure S5, Table S6). A summary of the results is shown in .
Figure 11. The bar chart of the number of differential flavonoid metabolites in each subclass in the ZJB_VS_ZSB, ZJF_VS_ZSF, ZJL_VS_ZSL, and ZJS_VS_ZSS models.
The relationship between biological processes and phenotypes can be clarified by monitoring changes in metabolites in different tissues and at various phases of development via plant metabolomics.[Citation51] In our study, the identity and content of flavonoid metabolites in the fruits, leaves, flowers, and seeds of DZ and LYZZ8 were analyzed using qualitative metabolomics. The content of most individual flavonoid metabolites was higher in the fruits of LYZZ8 than in the fruits of DZ, which was consistent with the findings of Xue,[Citation52] as well as the total flavonoid content in ZSF and ZJF. Among 14 up-regulated flavonoid metabolites in ZJL_VS_ZSL, the content of trilobatin, which was the highest among all up-regulated 14 metabolites in SJL, was only 2.69 µg/100 g, which was 1/4,000 of the content of nicotiflorin in SJL. The content of the remaining 49 flavonoid metabolites, including non-significant and down-regulated flavonoids, was lower in SJL than in ZJL, which was consistent with the total flavonoid content in ZSL and ZJL. In flowers, only the content of diosmin and (-)-epicatechin in ZJB exceeded 18 µg/100 g among the 19 down-regulated flavonoid metabolites in ZJB_VS_ZSB; the 30 up-regulated flavonoid metabolites in ZJB_VS_ZSB included several of the main flavonoid metabolites in flowers of DZ and LYZZ9, including phlorizin, phloretin, hesperidin, quercetin, and kaempferol. The flowers of LYZZ8 have a higher utilization value than DZ based on the content of individual flavonoid metabolites. In seeds, spinosin, the most predominant flavonoid metabolite, comprised more than 88% of the overall content of flavonoid metabolites, and the content of spinosin was significantly lower in ZSS than in ZJS. The content of up-regulated flavonoid metabolites was low in ZJS_VS_ZSS, with the exception of nicotiflorin. More studies of the flavonoids of the seeds of DZ are needed.
Identification of chemical markers for distinguishing the tissues of DZ and LYZZ8
The S-plot generated by the OPLS-DA model, which can be used to make comparisons between variable dimensions, is useful for filtering markers from “omics data;” the S-plot shows the relationship between the covariance p[1] and the correlation p(cor) for each variable.[Citation53] The constituents most likely to be responsible for the difference between samples are indicated by the variables that are close to the diagonal ends of “S” in the S-plot.[Citation35,Citation37,Citation38] In the S-plot based on the ZJF_VS_ZSF model, VIP is less than 1 for the variables in green, and VIP is greater than or equal to 1 for variables in red. Most of the variables were located near the center of “S;” with the exception of nicotiflorin, rutin, and (-)-catechin (which were located in the top-right corner of the “S,” indicating that the content of these three flavonoids was higher in ZSF than in ZJF) and (-)-epicatechin (which was located in the bottom-left corner of the “S,” indicating that the content of (-)-epicatechin was higher in ZJF than in ZSF) (). In addition, the VIP values of these four variables were all greater than 1; this indicates that these variables explain most of the difference between ZJF and ZSF. (-)-Epicatechin was excluded because of the lack of a significant difference between ZJF and ZSF. Our findings were consistent with those of Guo.[Citation35] Therefore, nicotiflorin, rutin, and (-)-catechin could be used as potential chemical markers for distinguishing between ZJF and ZSF.
Figure 12. The S-Plot based on ZJF_VS_ZSF OPLS-DA model. The red points in S-Plot indicate VIP ≥ 1, and the green points indicate VIP < 1.
The same method was applied to ZJL_VS_ZSL, ZJB_VS_ZSB, and ZJS_VS_ZSS. Quercitrin, phlorizin, and (-)-catechin were located in the bottom-left corner of the “S,” and narirutin was located in the top-right corner of the “S;” VIP values of these flavonoids were greater than 1, but narirutin was excluded given that its content did not significantly differ between ZJL and ZSL (). Thus, quercitrin, phlorizin, and (-)-catechin were used as potential chemical markers for distinguishing between ZJL and ZSL. Quercetin, hesperidin, kaempferol, and phlorizin were located in the top-right corner of the “S,” and catechin gallate was located in the bottom-left corner of the “S” (); the VIP values for both of these flavonoids were greater than 1, and significant differences in their content between ZJB and ZSB were observed. They could thus be used as chemical markers for distinguishing between ZJB and ZSB. Spinosin and nicotiflorin can be used as potential chemical markers for distinguishing between ZJS and ZSS ().
Figure 13. The S-Plot based on ZJL_VS_ZSL OPLS-DA model.
Figure 14. The S-Plot based on ZJB_VS_ZSB OPLS-DA model.
Figure 15. The S-Plot based on ZJS_VS_ZSS OPLS-DA model.
Discussion
Flavonoids are a significant class of secondary metabolites that play key physiological roles; they also have various uses in the food and medicine industries. Their patterns of accumulation vary among tissues in jujube and sour jujube. We evaluated the composition and content of flavonoid metabolites in different tissues of jujube and sour jujube. Flowers in the full-blossom period and fruits, leaves, and seeds in the semi-red stage of DZ and LYZZ8 were used as experimental materials. The fruit of DZ is crisp, juicy, and tasty[Citation54]; it is one of the most popular varieties in China and overseas.[Citation55] LYZZ8 is an excellent sour jujube variety with high yield, stress resistance, and seed kernel rate.[Citation56] A total of 101 flavonoid metabolites were qualitatively and quantitatively identified in the fruits, leaves, flowers, and seeds of DZ and LYZZ8 based on UPLC-ESI MS/MS metabolomics. Both PCA and HCA indicated that the fruits, seeds, and flowers of DZ and LYZZ8 were clustered on the basis of their metabolic profiles. An Upset plot revealed that amount of flavonoid metabolites in different tissues of DZ or LYZZ8 differed, the amount of flavonoid metabolites was similar in the same tissues of DZ and LYZZ8, and the tissue-specific flavonoid metabolites were identified. According to the PCA, HCA and Upset plot, the content and amount of flavonoid metabolites in diverse tissues of DZ and LYZZ8 varied, and subtle differences were observed among the same tissues of DZ and LYZZ8, which might stem from the consistency in the metabolic properties of the different tissues in DZ and LYZZ8.
The main flavonoid metabolites in ZJF are epicatechin, catechin, and rutin. Choi[Citation30]found that epicatechin was the most common flavonoid across the eight developmental stages of jujube fruits, and changes in the total flavonoid content in the eight fruit developmental stages were directly affected by changes in the content of epicatechin. Choi also found that the main flavonoids in the fruits of different jujube varieties were rutin and epicatechin.[Citation29] Xue[Citation46] studied the identity and content of flavonoids in 20 regional Chinese jujube fruits, and the results showed that epicatechin was the most common, followed by catechin, rutin, spinosin, quercetin, phlorizin, and luteolin, in all 20 fruits examined, which is consistent with the results of our study. Catechin, epicatechin, and rutin are the major flavonoid metabolites in ZSF, and slight differences in these flavonoids were observed between ZSF and ZJF. Flavonoids were more abundant in ZSF than in ZJF, which is consistent with the results of Xue.[Citation57,Citation58] The flavonoids of ZJF merit increased research attention.
Quercitrin, nicotiflorin, rutin, and phlorizin are major flavonoid metabolites in ZJL and ZSL. Guo[Citation36] characterized differences in the bioactive compounds in the leaves of six jujube varieties and two sour jujube varieties from different regions in China and found that nicotiflorin was the most abundant flavonoid (ranging from 15.78 to 32.72 mg/g DW); no differences in the content of nicotiflorin between sour jujube and jujube were observed. An HPLC analysis of sour jujube leaves from Hebei Province, China mainly contained rutin and hesperidin,[Citation59] whereas an LC-MS/MS analysis of sour jujube leaves from Shanxi Province, China mainly comprised rutin and quercetin-3-O-robinobioside.[Citation60] A previous HPLC-UV analysis of the leaves of 66 jujube varieties from Xinjiang Province, China[Citation61] has shown that the content of quercetin-3-O-arabinose-rhamnoside and rutin peaked at 13.66 mg/g and 8.13 mg/g DW. Zhang[Citation62] found that the leaves of Junzao mainly contained quercetin-3-O-β-1-arabinose-α-1-rhamnoside (13.912 mg/g DW) and kaempferol-3-O-sophorside (2.987 mg/g DW); this finding was confirmed by Song.[Citation63] Xue[Citation46] found that quercetin (4.427 mg/g) and rutin (4.095 mg/g FW) were the major flavonoids in the leaves of Huping Zao grown in Shanxi Province, China. Wang[Citation64] studied the flavonoids in sour jujube leaves from Baoding at different developmental stages and found that catechin (34.48 mg/g) and epigallocatechin (19.15 mg/g DW) were the dominant flavonoids at all stages. Our findings were inconsistent with the results of these previous studies, which might stem from the different varieties used and differences in geographical and climate conditions. Although harsh environmental conditions have been shown to increase flavonoid levels, variety has a major effect on the composition and content of flavonoids in the leaves of jujube and sour jujube.[Citation14]
Numerous studies have shown that the seeds of sour jujube have hypnotic and sedative activities; the seeds of jujube have received less attention because of their high abortion rate and low kernel percentage.[Citation65] Previous studies have focused on identifying flavonoids in the seeds of sour jujube[Citation39,Citation66,Citation67] and a few well-known compounds have been identified[Citation68–70]; however, large-scale qualitative and quantitative analyses of the bioactive compounds in sour jujube seeds have not yet been conducted. A comparative study aimed at identifying flavonoids for distinguishing sour jujube seeds from YNSZ (Ziziphus mauritiana Hu.) seeds[Citation37] indicated that spinosin (73.11 mg/g) and 6’’-feruloylspinosin (30.81 mg/g) were common in sour jujube seeds. The average content of spinosin and 6’’-feruloylspinosin in jujube seeds grown in Korea[Citation29] was 17.71 mg/g and 12.40 mg/g DW, respectively; the content of other flavonoids, such as swertisin and vitexin, was only 1/1,000 of the content of spinosin and 6’’-feruloylspinosin. In our study, the content of spinosin was nearly two times higher in ZJS than in ZSS, which indicates that ZJS might be more rewarding in functional and medical applications, and more comprehensive research should be conducted in the future. The results of our study provide new insights into the composition and content of flowers in jujube and sour jujube. Flavonoid metabolites were most abundant in ZJB and ZSB; the content of quercetin, hesperidin, hydroxysafflor yellow A, kaempferol, phloretin, diosmin, hyperoside, eriodictyol, and tiliroside was much higher in ZJB and ZSB than in the other tissues in DZ and LYZZ8. Additional studies are needed to clarify differences in the regulatory mechanisms of these flavonoids between flowers and other tissues as well as the medical and biological significance of these differences.
Metabolomics combined with multivariate statistical analysis, such as PLS-DA and OPLS-DA, has become a powerful tool for identifying differential metabolites in different metabolic status and identifying the possible biomarkers for differentiating among diverse samples and tissues.[Citation71,Citation72] A UPLC-QTOF-MS metabolomics approach was used to investigate the active compounds in pseudo-ginseng,[Citation73] eight strong bioactive chemical markers were identified in the branches of pseudo-ginseng. Wu[Citation74] examined the metabolic profiles of six tissues of Polygonum cuspidatum using LC-MS and identified 13 potential chemical markers for distinguishing among six tissues using OPLS-DA. Chang[Citation75] identified 23 chemical markers for distinguishing among tissues in balloon flower roots by OPL-DA based on untargeted metabolomics. Rutin was identified as a chemical marker for distinguishing among the fruits of 26 dried jujube varieties from five producing areas in China.[Citation40] We screened eight and ten potential chemical markers for different tissues of DZ and LYZZ8 through PLS-DA and obtained p-values for multiple comparisons, respectively. We also identified several key chemical markers for distinguishing among the same tissues of DZ and LYZZ8 based on the S-plot generated by the OPLS-DA models. These potential chemical markers can be used to identify functional genes with transcriptome and genome data.
In the light of the current research, the following works should be conducted: the composition and content of flavonoid metabolites in diverse tissues of more jujube and sour jujube varieties in different cultivation areas will be analyzed to find out the changes that what extent of varieties and areas affect our findings as well as the verification of chemical markers. It perhaps offers a valuable information for utilization of different tissues of jujube and sour jujube when developing flavonoid functional products.
Conclusion
We examined the identity and content of flavonoid metabolites in the fruits, leaves, flowers, and seeds of DZ and LYZZ8 using UPLC-ESI-MS/MS metabolomics. A total of 101 flavonoid metabolites were identified via qualitative and quantitative analyses. Flavonoid accumulation patterns varied among tissues of DZ and LYZZ8, and similar flavonoid accumulation patterns were observed in the fruits, flowers, and seeds in DZ and LYZZ8 according to the results of PCA and HCA. The UpSet plot revealed that the number of flavonoid metabolites in different tissues of DZ and LYZZ8 varied; flavonoid metabolites were most abundant in flowers, followed by the seeds, leaves, and fruits. Tissue-specific flavonoid metabolites were also identified. The main flavonoid metabolites varied among tissues. (-)-Epicatechin, (-)-catechin, and rutin were the main flavonoids in the fruits of DZ and LYZZ8; quercitrin, nicotiflorin, rutin, and phlorizin were the main flavonoids in the leaves of DZ and LYZZ8; spinosin was the main flavonoid in the seeds of DZ and LYZZ8; rutin, nicotiflorin, quercitrin, and quercetin were the main flavonoids in the flowers of DZ; and quercitrin, rutin, hesperidin, and phlorizin were the main flavonoids in the flowers of LYZZ8. Potential chemical markers for differentiating among tissues in DZ and LYZZ8 as well as the same tissues of DZ and LYZZ8 were identified by PLS-DA and S-plot based on OPLS-DA, respectively. Overall, our findings provided new insights with implications for the use of jujube and sour jujube resources in the pharmaceutical and functional food industries.
Author contributions
Writing-original draft: Xiaojing Qiu; methodology: Xiaoling Wang, Xumao LI; software: Xiaojing Qiu; investigation: Xiaojing Qiu, Xumao Li; writing-review and editing: Xiaoling Wang, Yongmin Mao, Lianying Shen.
Acknowledgments
The authors sincerely thank Da Ming Jujube Germplasm Repository for providing the appropriately different tissues of jujube and sour jujube.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
References
- Chen, J. P.; Li, Z. G.; Maiwulanjiang, M.; Zhang, W. L.; Zhan, J. Y.; Lam, T. W.; Zhu, K. Y.; Yao, P.; Choi, R. C. Y.; Lau, D. T. W., et al. Chemical and Biological Assessment of Ziziphus Jujuba Fruits from China: Different Geographical Sources and Developmental Stages. J. Agric. Food. Chem. 2013, 61(30), 7315–7324. DOI: 10.1021/jf402379u.
- Qu, Z. Z.; Wang, Y. H. Fruits Tree Records of China; China Forestry Publication: House, Beijing, China, 1993, Vol. Chinese jujube volume.
- Liu, M. J.; Wang, J. R.; Wang, L. L.; Liu, P.; Zhao, J.; Zhao, Z. H.; Yao, S. R.; Stanica, F.; Liu, Z. G.; Wang, L. X., et al. The Historical and Current Research Progress on Jujube–A Superfruit for the Future. Hortic. Res. 2020, 7(1), 119. DOI: 10.1038/s41438-020-00346-5.
- Gao, Q. H.; Wu, C. S.; Wang, M. The Jujube (Ziziphus Jujuba Mill.) Fruit: A Review of Current Knowledge of Fruit Composition and Health Benefits. J. Agric. Food. Chem. 2013, 61(14), 3351–3363. DOI: 10.1021/jf4007032.
- Wang, B. N.; Liu, H. F.; Zheng, J. B.; Fan, M. T.; Cao, W. Distribution of Phenolic Acids in Different Tissues of Jujube and Their Antioxidant Activity. J. Agric. Food. Chem. 2011, 59(4), 1288–1292. DOI: 10.1021/jf103982q.
- Damiano, S.; Forino, M.; De, A.; Vitali, L. A.; Lupidi, G.; Taglialatela-Scafati, O. Antioxidant and Antibiofilm Activities of Secondary Metabolites from Ziziphus Jujuba Leaves Used for Infusion Preparation. Food Chem. 2017, 230, 24–29. DOI: 10.1016/j.foodchem.2017.02.141.
- Jiang, T. Y.; He, F.; Han, S. W.; Chen, C.; Zhang, Y. N.; Che, H. L. Characterization of cAMP as an Anti-Allergic Functional Factor in Chinese Jujube (Ziziphus Jujuba Mill.). J. Funct. Foods. 2019, 60. DOI: 10.1016/j.jff.2019.06.016.
- Zhan, R.; Xia, L.; Shao, J. H.; Wang, C.; Chen, D. F. Polysaccharide Isolated from Chinese Jujube Fruit (Zizyphus jujuba Cv. Junzao) Exerts Anti-Inflammatory Effects Through MAPK Signaling. J. Funct. Foods. 2018, 40, 461–470. DOI: 10.1016/j.jff.2017.11.026.
- Hovanet, M. V.; Ozon, E. A.; Morosan, E.; Seremet, O. C.; Oprea, E.; Geana, E. I.; Anghel, A. I.; Badiceanu, C.; Dutu, L. E.; Stoicescu, C. S., et al. Wound Healing and Anti-Inflammatory Effects of a Newly Developed Ointment Containing Jujube Leaves Extract. Life. 2022, 12(12), 1947. DOI: 10.3390/life12121947.
- Wu, Z.; Gao, R. P.; Li, H.; Wang, Y. D.; Luo, Y.; Zou, J.; Zhao, B.; Chen, S. Q. New Insight into the Joint Significance of Dietary Jujube Polysaccharides and 6-Gingerol in Antioxidant and Antitumor Activities. R.S.C. Adv. 2021, 11(53), 33219–33234. DOI: 10.1039/d1ra03640h.
- Zhong, Y. J.; Luo, R. L.; Liu, Q.; Zhu, J. C.; Lei, M.; Liang, X. F.; Wang, X.; Peng, X. L. Jujuboside a Ameliorates High Fat Diet and Streptozotocin Induced Diabetic Nephropathy via Suppressing Oxidative Stress, Apoptosis, and Enhancing Autophagy. Food. Chem. Toxicol. 2022, 159, 112697. DOI: 10.1016/j.fct.2021.112697.
- Liu, N.; Yang, M.; Huang, W. Z.; Wang, Y. J.; Yang, M.; Wang, Y.; Zhao, Z. X. Composition, Antioxidant Activities and Hepatoprotective Effects of the Water Extract of Ziziphus Jujuba Cv. Jinsixiaozao. RSC Adv. 2017, 7(11), 6511–6522. DOI: 10.1039/c6ra27516h.
- Wang, L. H.; Luo, Z.; Liu, Z. G.; Zhao, J.; Deng, W. P.; Wei, H. R.; Liu, P.; Liu, M. J. Genome Size Variation within Species of Chinese Jujube (Ziziphus Jujuba Mill.) and Its Wild Ancestor Sour Jujube (Z. Acidojujuba Cheng Et Liu). Forests. 2019, 10(5), 460–476. DOI: 10.3390/f10050460.
- Sun, Y. F.; Liang, Z. S.; Shan, C. J.; Viernstein, H.; Unger, F. Comprehensive Evaluation of Natural Antioxidants and Antioxidant Potentials in Ziziphus Jujuba Mill. Var. Spinosa (Bunge) Hu Ex H. F. Chou Fruits Based on Geographical Origin by TOPSIS Method. Food Chem. 2011, 124(4), 1612–1619. DOI: 10.1016/j.foodchem.2010.08.026.
- Li, S. P.; Deng, B. L.; Tian, S.; Guo, M. X.; Liu, H. X.; Zhao, X. S. Metabolic and Transcriptomic Analyses Reveal Different Metabolite Biosynthesis Profiles Between Leaf Buds and Mature Leaves in Ziziphus Jujuba Mill. Food Chem. 2021, 347, 129005. DOI: 10.1016/j.foodchem.2021.129005.
- Shen, L. Y.; Luo, H.; Wang, X. L.; Wang, X. M.; Qiu, X. J.; Liu, H.; Zhou, S. S.; Jia, K. H.; Nie, S.; Bao, Y. T., et al. Chromosome-Scale Genome Assembly for Chinese Sour Jujube and Insights into Its Genome Evolution and Domestication Signature. Front Plant Sci. 2021, 12, 773090. DOI: 10.3389/fpls.2021.773090.
- Xiao, F. Q.; Shao, S.; Zhang, H. Y.; Li, G. F.; Piao, S. L.; Zhao, D. Q.; Li, G. Z.; Yan, M. M. Neuroprotective Effect of Ziziphi Spinosae Semen on Rats with P-Chlorophenylalanine-Induced Insomnia via Activation of GABA(A) Receptor. Front Pharmacol. 2022, 13, 965308. DOI: 10.3389/fphar.2022.965308.
- Shi, Q. Q.; Han, G.; Liu, Y.; Jiang, J. J.; Jia, Y. Y.; Li, X. G. Nutrient Composition and Quality Traits of Dried Jujube Fruits in Seven Producing Areas Based on Metabolomics Analysis. Food Chem. 2022, 385, 132627. DOI: 10.1016/j.foodchem.2022.132627.
- Xu, T.; Zhou, X. L.; Degen, A.; Yin, J. L.; Zhang, S. X.; Chen, N. The Inclusion of Jujube By-Products in Animal Feed: A Review. Sustainability. 2022, 14(13), 7882–7891. DOI: 10.3390/su14137882.
- Li, J. W.; Fan, L. P.; Ding, S. D.; Ding, X. L. Nutritional Composition of Five Cultivars of Chinese Jujube. Food Chem. 2007, 103(2), 454–460. DOI: 10.1016/j.foodchem.2006.08.016.
- Zhang, R. T.; Chen, J. H.; Shi, Q.; Li, Z. Y.; Peng, Z. Y.; Zheng, L.; Wang, X. R. Quality Control Method for Commercially Available Wild Jujube Leaf Tea Based on HPLC Characteristic Fingerprint Analysis of Flavonoid Compounds. J. Sep. Sci. 2014, 37(1–2), 45–52. DOI: 10.1002/jssc.201300841.
- Jiang, N.; Hou, S. J.; Liu, Y. Y.; Ren, P. X.; Xie, N. Y.; Yuan, Y.; Hao, Q.; Liu, M. J.; Zhao, Z. H. Combined LC-MS-Based Metabolomics and GC-IMS Analysis Reveal Changes in Chemical Components and Aroma Components of Jujube Leaf Tea During Processing. Front Plant Sci. 2023, 14, 1179553. DOI: 10.3389/fpls.2023.1179553.
- Jiang, X.; Duan, L.; Bao, G. H.; Wu, X. L. Preparation and Characterization of Spine Date Seed Oil Microcapsules. Cereals Oils. 2020, 33(9), 56–59. DOI: 10.3969/j.issn.1008-9578.2020.09.013.
- Zhao, A. L.; Xue, X. F.; Wang, Y. K. Characteristic Analysis of Sugars and Organic Acids Components and Contents of Chinese Jujube and Wild Jujube Fruits. J. Tarim Univ. 2016, 28(3), 29–36. DOI: 10.3969/j.issn.1009-0568.2016.03.006.
- Zhou, H. L.; Weng, Y.; Shao, Y. L. Quality Evaluation of Ziziphi Spinosae Semen in Different Harvest Periods Based on HPLC Fingerprints Combined with Chemical Pattern Recognition. Chinese Journal Of Information On TCM. 2021, 9, 406–413. DOI: 10.19879/j.cnki.1005-5304.202105217.
- Hu, P. F.; Suriguga Zhao, M.; Chen, S. Q.; Wu, X. H.; Wan, Q. Transcriptional Regulation Mechanism of Flavonoids Biosynthesis Gene During Fruit Development in Astragalus Membranaceus. Front. Genet. 2022, 13, 972990. DOI: 10.3389/fgene.2022.972990.
- Shen, N.; Wang, T. F.; Gan, Q.; Liu, S.; Wang, L.; Jin, B. Plant Flavonoids: Classification, Distribution, Biosynthesis, and Antioxidant Activity. Food Chem. 2022, 383, 132531. DOI: 10.1016/j.foodchem.2022.132531.
- Tohge, T.; Perez de Souza, L.; Fernie, A. R. On the Natural Diversity of Phenylacylated-Flavonoid and Their in Planta Function Under Conditions of Stress. Phytochem Rev. 2018, 17(2), 279–290. DOI: 10.1007/s11101-017-9531-3.
- Choi, S. H.; Ahn, J. B.; Kozukue, N.; Levin, C. E.; Friedman, M. Distribution of Free Amino Acids, Flavonoids, Total Phenolics, and Antioxidative Activities of Jujube (Ziziphus jujuba) Fruits and Seeds Harvested from Plants Grown in Korea. J. Agric. Food. Chem. 2011, 59(12), 6594–6604. DOI: 10.1021/jf200371r.
- Choi, S. H.; Ahn, J. B.; Kim, H. J.; Im, N. K.; Kozukue, N.; Levin, C. E.; Friedman, M. Changes in Free Amino Acid, Protein, and Flavonoid Content in Jujube (Ziziphus jujube) Fruit During Eight Stages of Growth and Antioxidative and Cancer Cell Inhibitory Effects by Extracts. J. Agric. Food. Chem. 2012, 60(41), 10245–10255. DOI: 10.1021/jf302848u.
- Cosmulescu, S.; Trandafir, I.; Nour, V.; Achim, G.; Botu, M.; Iordanescu, O. Variation of Bioactive Compounds and Antioxidant Activity of Jujube (Ziziphus jujuba) Fruits at Different Stages of Ripening. Not. Bot. Horti Agrobot. Cluj-Napoca. 2018, 46(1), 134–137. DOI: 10.15835/nbha46110752.
- Yi, L.; Liang, Z. T.; Peng, Y.; Yao, X.; Chen, H. B.; Zhao, Z. Z. Tissue-Specific Metabolite Profiling of Alkaloids in Sinomenii Caulis Using Laser Microdissection and Liquid Chromatography-Quadrupole/time of Flight-Mass Spectrometry. J. Chromatogr. A. 2012, 1248, 93–103. DOI: 10.1016/j.chroma.2012.05.058.
- Liang, Z.; Sham, T.; Yang, G. Y.; Yi, L.; Chen, H. B.; Zhao, Z. Z. Profiling of Secondary Metabolites in Tissues from Rheum Palmatum L. Using Laser Microdissection and Liquid Chromatography Mass Spectrometry. Anal. Bioanal. Chem. 2013, 405(12), 4199–4212. DOI: 10.1007/s00216-013-6819-z.
- Zhao, Y. J.; Chu, S. S.; Gui, S. Y.; Qin, Y. J.; Xu, R.; Shan, T. Y.; Peng, H. S. Tissue-Specific Metabolite Profiling of Fallopia Multiflora (Heshouwu) and Fallopia Multiflora Var. Angulata by Mass Spectrometry Imaging and Laser Microdissection Combined with UPLC-Q/TOF-MS. J. Pharm. Biomed. Anal. 2021, 200, 114070. DOI: 10.1016/j.jpba.2021.114070.
- Guo, S.; Duan, J. A.; Tang, Y. P.; Qian, D. W.; Zhu, Z. H.; Qian, Y. F.; Shang, E. X.; Su, S. L. UHPLC-TOFMS Coupled with Chemometric Method as a Powerful Technique for Rapid Exploring of Differentiating Components Between Two Ziziphus Species. J. Sep. Sci. 2011, 34(6), 659–666. DOI: 10.1002/jssc.201000788.
- Guo, S.; Duan, J. A.; Tang, Y. P.; Qian, Y. F.; Zhao, J. L.; Qian, D. W.; Su, S. L.; Shang, E. X. Simultaneous Qualitative and Quantitative Analysis of Triterpenic Acids, Saponins and Flavonoids in the Leaves of Two Ziziphus Species by HPLC-PDA-MS/ELSD. J. Pharm. Biomed. Anal. 2011, 56(2), 264–270. DOI: 10.1016/j.jpba.2011.05.025.
- Guo, S.; Duan, J. A.; Li, Y. Q.; Wang, R. Q.; Yan, H.; Qian, D. W.; Tang, Y. P.; Su, S. L. Comparison of the Bioactive Components in Two Seeds of Ziziphus Species by Different Analytical Approaches Combined with Chemometrics. Front Pharmacol. 2017, 8, 609. DOI: 10.3389/fphar.2017.00609.
- Zhang, F. X.; Li, M.; Qiao, L. R.; Yao, Z. H.; Li, C.; Shen, X. Y.; Wang, Y.; Yu, K.; Yao, X. S.; Dai, Y. Rapid Characterization of Ziziphi Spinosae Semen by UPLC/Qtof MS with Novel Informatics Platform and Its Application in Evaluation of Two Seeds from Ziziphus Species. J. Pharm. Biomed. Anal. 2016, 122, 59–80. DOI: 10.1016/j.jpba.2016.01.047.
- Yan, Y.; Shen, C. X.; Pei, X. P.; Li, A. P.; Du, H. Z.; Qin, X. M.; Du, C. H. Compare the Chemical Profiling of Ziziphi Spinosae Semen and Ziziphi Mauritianae Semen Using Untargeted Metabolomics. J. Food Compos. Anal. 2020, 94, 103635. DOI: 10.1016/j.jfca.2020.103635.
- Wang, C.; Wang, R.; Fu, C. D.; Jiang, X. X.; Li, X. G.; Han, G.; Zhang, J. F. Combining Bioactive Compounds and Antioxidant Activity Profiling Provide Insights into Assessment of Geographical Features of Chinese Jujube. Food Biosci. 2022, 46, 101573. DOI: 10.1016/j.fbio.2022.101573.
- Peng, B.; Li, H.; Peng, X. X. Functional Metabolomics: From Biomarker Discovery to Metabolome Reprogramming. Protein Cell. 2015, 6(9), 628–637. DOI: 10.1007/s13238-015-0185-x.
- Shi, Q. Q.; Du, J. T.; Zhu, D. J.; Li, X.; Li, X. G. Metabolomic and Transcriptomic Analyses of Anthocyanin Biosynthesis Mechanisms in the Color Mutant Ziziphus Jujuba Cv. Tailihong. J. Agric. Food. Chem. 2020, 68(51), 15186–15198. DOI: 10.1021/acs.jafc.0c05334.
- Shi, Q. Q.; Li, X.; Du, J. T.; Liu, Y.; Shen, B. Q.; Li, X. G. Association of Bitter Metabolites and Flavonoid Synthesis Pathway in Jujube Fruit. Front Nutr. 2022, 9, 901756. DOI: 10.3389/fnut.2022.901756.
- Zhang, Q.; Wang, L. L.; Liu, Z. G.; Zhao, Z. H.; Zhao, J.; Wang, Z. T.; Zhou, G. F.; Liu, P.; Liu, M. J. Transcriptome and Metabolome Profiling Unveil the Mechanisms of Ziziphus Jujuba Mill. Peel Coloration. Food Chem. 2020, 312, 125903. DOI: 10.1016/j.foodchem.2019.125903.
- Thevenot, E. A.; Roux, A.; Xu, Y.; Ezan, E.; Junot, C. Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Massi Ndex, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. J. Proteome Res. 2015, 14(8), 3322–3335. DOI: 10.1021/acs.jproteome.5b00354.
- Xue, X. F.; Zhao, A. L.; Wang, Y. K.; Ren, H. Y.; Du, J. J.; Li, D. K.; Li, Y.; Sarker, U. Composition and Content of Phenolic Acids and Flavonoids Among the Different Varieties, Development Stages, and Tissues of Chinese Jujube (Ziziphus Jujuba Mill.). Plos One. 2021, 16(10), e0254058. DOI: 10.1371/journal.pone.0254058.
- Bao, K. D.; Li, P.; Qi, L. W.; Li, H. J.; Yi, L.; Wang, W.; Wang, Y. Q. Characterization of Flavonoid Metabolites in Rat Plasma, Urine, and Feces After Oral Administration of Semen Ziziphi Spinosae Extract by Hplc-Diode-Array Detection (Dad) and Ion-Trap Mass spectrometry(msn). Chem. Pharm. Bull. 2009, 57(2), 144–148. DOI: 10.1248/cpb.57.144.
- Khalil, M. N. A.; Fekry, M. I.; Farag, M. A. Metabolome Based Volatiles Profiling in 13 Date Palm Fruit Varieties from Egypt via SPME GC-MS and Chemometrics. Food Chem. 2017, 217, 171–181. DOI: 10.1016/j.foodchem.2016.08.089.
- Yuan, H. J.; Zeng, X. Q.; Shi, J.; Xu, Q. J.; Wang, Y. L.; Jabu, D. Z.; Sang, Z.; Nyima, T. Time-Course Comparative Metabolite Profiling Under Osmotic Stress in Tolerant and Sensitive Tibetan Hulless Barley. BioMed Research International. 2018, 2018, 1–12. DOI: 10.1155/2018/9415409.
- Li, Q. Q.; Li, B.; Zhang, R.; Liu, S. Y.; Yang, S. P.; Li, Y.; Li, J. X. Flavoromics Approach in Critical Aroma Compounds Exploration of Peach: Correlation to Origin Based on OAV Combined with Chemometrics. Foods. 2023, 12(4), 837–851. DOI: 10.3390/foods12040837.
- Matus, J. T. Transcriptomic and Metabolomic Networks in the Grape Berry Illustrate That It Takes More Than Flavonoids to Fight Against Ultraviolet Radiation. Front Plant Sci. 2016, 7, 1337. DOI: 10.3389/fpls.2016.01337.
- Xue, X. F.; Zhao, A. L.; Wang, Y. K.; Ren, H. Y.; Li, Y.; Li, D. K.; Du, J. J. Metabolomics-Based Analysis of Flavonoid Metabolites in Chinese Jujube and Sour Jujube Fruits from Different Harvest Periods. J. Food Sci. 2022, 87(9), 3752–3765. DOI: 10.1111/1750-3841.16290.
- El Senousy, A. S.; Farag, M. A.; Al-Mahdy, D. A.; Wessjohann, L. A. Developmental Changes in Leaf Phenolics Composition from Three Artichoke Cvs. (Cynara scolymus) as Determined via UHPLC-MS and Chemometrics. Phytochemistry. 2014, 108, 67–76. DOI: 10.1016/j.phytochem.2014.09.004.
- Zhang, Q.; Wang, L. L.; Wang, Z. T.; Liu, Z. G.; Zhao, Z. H.; Zhou, G. F.; Liu, M. J.; Liu, P. Variations of the Nutritional Composition of Jujube Fruit (Ziziphus Jujuba Mill.) During Maturation Stages. Int. J. Food. Prop. 2020, 23(1), 1066–1081. DOI: 10.1080/10942912.2020.1770281.
- Song, L. H.; Meinhardt, L. W.; Bailey, B.; Zhang, D. P. Genetic Improvement of Chinese Jujube for Disease Resistances: Status, Knowledge Gaps and Research Needs. Crop Breeding, Genet. Genomics. 2019, 1, e190015. DOI: 10.20900/cbgg20190015.
- Wang, X. L.; Qiu, X. J.; Li, X. M.; Liu, S. Y. “Liyuan Zhenzhu 8”, a New Sour Jujube Cultivar. Acta Horticulturae Sinica. 2022, 48(S2), 61–62. DOI: 10.16420/j.issn.0513-353x.2022-0073.
- Xue, X. F.; Gong, G. H.; Zhao, A. L.; Ren, H. K.; Wang, Y. K.; Li, D. K. Comparison of Flavonoid Contents and Antioxidant Capacities in Different Organs of Chinese Jujube and Wild Jujube. China Fruits. 2020, 1(12), 59–63. DOI: 10.16626/j.cnki.issn1000-8047.2020.01.012.
- Xue, X. F.; Zhao, A. L.; Wang, Y. K.; Ren, H. Y.; Gong, G. H.; Li, D. K. Analysis of Flavonoid Content and Antioxidant Activity in Jujube Germplasms Fruits. Non-Wood Forest Research. 2020, 38(3), 113–118. DOI: 10.14067/j.cnki.1003-8981.2020.03.013.
- Wang, X.; Yang, M. T.; Gao, L.; Cao, W. L.; Niu, L. Y. A Research on the Index Component Measurement of the Extracts from Different Parts of Wild Jujube and Their Antioxidant Activities in vitro. Modern Tradi.Chinese Medi. 2020, 40(4), 13–19. DOI: 10.13424/j.cnki.mtcm.2020.04.004.
- Fu, C.; Cui, X. F.; Pei, X. P.; Du, C. H.; Yan, Y. Simultaneous Determination of Six Chemical Components in Ziziphi Spinosae Folium by LC-MS/MS. J. Shanxi Med. Univ. 2020, 51(7), 666–673. DOI: 10.13753/j.issn.1007-6611.2020.07.010.
- Song, L. J.; Zheng, J.; Zhang, L.; Yan, S. J.; Huang, W. J.; He, J.; Liu, P. Z. Phytochemical Profiling and Fingerprint Analysis of Chinese Jujube (Ziziphus Jujuba Mill.) Leaves of 66 Cultivars from Xinjiang Province. Molecules. 2019, 24(24), 4528. DOI: 10.3390/molecules24244528.
- Zhang, L.; Liu, P. Z.; Li, L. L.; Huang, Y. F.; Pu, Y.; Hou, X. J.; Song, L. J. Identification and Antioxidant Activity of Flavonoids Extracted from Xinjiang Jujube (Ziziphus Jujube Mill.) Leaves with Ultra-High Pressure Extraction Technology. Molecules. 2019, 24(1), 122. DOI: 10.3390/molecules24010122.
- Song, L. J.; Liu, P. Z.; Yan, Y. Z.; Huang, Y.; Bai, B. Y.; Hou, X. J.; Zhang, L. Supercritical CO2 Fluid Extraction of Flavonoid Compounds from Xinjiang Jujube (Ziziphus Jujuba Mill.) Leaves and Associated Biological Activities and Flavonoid Compositions. Ind. Crops Prod. 2019, 139, 111508. DOI: 10.1016/j.indcrop.2019.111508.
- Wang, Z. J.; An, X. W.; Chitrakar, B.; Li, J. M.; Yuan, Y.; Liu, K. X.; Nie, X. Y.; Zhang, Y. F.; Zhao, X.; Zhao, Z. H., et al. Spatial and Temporal Distribution of Phenolic and Flavonoid Compounds in Sour Jujube (Ziziphus. Acidojujuba Cheng Et Liu) and Their Antioxidant Activities. Plant Foods Hum. Nutr. 2023, 78(1), 46–51. DOI: 10.1007/s11130-022-01015-9.
- Wang, D.; Xiong, R. C.; Wu, C. Y.; Gao, J. S. Jujube Breeding Research. J. Tarim Univ. 2016, 28(3), 109–118. DOI: 10.3969/j.issn.1009-0568.2016.03.019.
- Li, L. M.; Liao, X.; Peng, S. L.; Ding, L. S. Chemical Constituents from the Seeds of Ziziphus Jujuba Var. Spinosa (Bunge) Hu. J. Integr. Plant Biol. 2005, 47(4), 494–498. DOI: 10.1111/j.1744-7909.2005.00060.x.
- Li, M.; Wang, Y.; Tsoi, B.; Jin, X. J.; He, R. R.; Yao, X. J.; Dai, Y.; Kurihara, H.; Yao, X. S. Indoleacetic Acid Derivatives from the Seeds of Ziziphus Jujuba Var. Spinosa. Fitoterapia. 2014, 99, 48–55. DOI: 10.1016/j.fitote.2014.09.001.
- Liu, X.; Zhu, X. C.; Zhu, H.; Xie, L.; Ma, J.; Xu, Y. H.; Zhou, Q. G.; Wu, Z. J.; Cai, B. C. Simultaneous Quantification of Six Bioactive Components in Decoction of Ziziphi Spinosae Semen Using Ultrahigh Performance Liquid Chromatography Coupled with Triple-Quadrupole Mass Spectrometry. Journal Of Analytical Methods In Chemistry. 2018, 2018, 1–6. DOI: 10.1155/2018/8397818.
- Wu, B. A.; Qu, C. H.; Wang, Y. X.; Zhao, J. F.; Du, H. Z. Comparison of the Quenching Effects of Two Main Components of Ziziphi Spinosae Semen on Serum Albumin Fluorescence. J. Fluoresc. 2019, 29(5), 1113–1123. DOI: 10.1007/s10895-019-02422-z.
- Zhao, X. S.; Xie, L. W.; Wu, H. F.; Kong, W. J.; Yang, M. H. Analysis of Six Bioactive Components in Semen Ziziphi Spinosae by UPLC-ELSD and UPLC-Q/TOF-MS. Anal. Methods. 2014, 6(15), 5856–5864. DOI: 10.1039/c4ay00800f.
- Zhang, L.; Huang, Y.; Lian, M. J.; Fan, Z. J.; Tian, Y. Q.; Wang, Y. F.; Kang, H.; Liu, S.; Liu, S. Y.; Li, T., et al. Metabolic Profiling of Hepatitis B Virus-Related Hepatocellular Carcinoma with Diverse Differentiation Grades. Oncol. Lett. 2017, 13(3), 1204–1210. DOI: 10.3892/ol.2017.5596.
- Nickler, M.; Ottiger, M.; Steuer, C.; Huber, A.; Anderson, J. B.; Muller, B.; Schuetz, P. Systematic Review Regarding Metabolic Profiling for Improved Pathophysiological Understanding of Disease and Outcome Prediction in Respiratory Infections. Respir. Res. 2015, 16(1), 125–150. DOI: 10.1186/s12931-015-0283-6.
- Li, P.; AnandhiSenthilkumar, H.; Wu, S. B.; Liu, B.; Guo, Z. Y.; Fata, J. E.; Kennelly, E. J.; Long, C. L. Comparative UPLC-QTOF-MS-Based Metabolomics and Bioactivities Analyses of Garcinia Oblongifolia. Journal Of Chromatography B. 2016, 1011, 179–195. DOI: 10.1016/j.jchromb.2015.12.061.
- Wu, Z. J.; Wang, X. W.; Chen, M.; Hu, H. Y.; Cao, J.; Chai, T. Y.; Wang, H. A Study on Tissue-Specific Metabolite Variations in Polygonum Cuspidatum by High-Resolution Mass Spectrometry-Based Metabolic Profiling. Molecules. 2019, 24(6), 1058. DOI: 10.3390/molecules24061058.
- Chang, X. W.; Li, J.; Ju, M. Z.; Yu, H. W.; Zha, L. P.; Peng, H. S.; Wang, J. T.; Peng, D. Y.; Gui, S. Y. Untargeted Metabolomics Approach Reveals the Tissue-Specific Markers of Balloon Flower Root (Platycodi Radix) Using UPLC-Q-TOF/MS. Microchem. J. 2021, 168, 106447. DOI: 10.1016/j.microc.2021.106447.