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BY 4.0 license Open Access Published by De Gruyter Open Access December 6, 2022

Phytochemical analysis of Ziziphus jujube leaf at different foliar ages based on widely targeted metabolomics

  • Hongxia Liu , Lefei Wang , Mingyue Xu , Benliang Deng , Hui Liu and Xusheng Zhao EMAIL logo
From the journal Open Chemistry

Abstract

Based on metabolomics, the metabolites of Jujube leaves LS1 (one bud and two leaves), LS2 (one bud and three leaves), and LS3 (one bud and four leaves) were taken and examined by ultra-high performance liquid chromatography-mass spectrometry technique. There were 22 substance categories that were identified. Principal component analysis was also utilized to distinguish the metabolomics at the three different foliar ages, and the results suggested that the samples at different foliar ages were clearly separated, demonstrating that the metabolites in the three foliar ages were significantly different. Through the screening of differential metabolites and hierarchical clustering analysis, our results suggested that the composition and the content distribution of the differential metabolites at three different foliar ages were significantly different. In the LS1, delphinidin, N-hydroxy tryptamine, serotonin, methylquercetin O-hexoside, tricin 7-O-hexoside, and eriodictyol C-hexoside were identified as the distinctive compounds. In the LS2, N-caffeoyl agmatine, lysoPC 18:3 (2n isomer), N-(4′-O-glycosyl)-p-coumaroyl-agmatine, dihydromyricetin, and hydroxy-methoxycinnamate were identified as the distinctive compounds. Similarly, the 3-O-p-coumaroyl-quinic acid, O-feruloyl 4-hydroxylcoumarin, isorhamnetin 3-O-neohesperidoside, cyanidin 3-O-galactoside, quercetin O-acetylhexoside, and DIMBOA glucoside were identified as the distinctive compounds in LS3. These characteristic compounds could provide a strong theoretical basis for rapid identification of jujube leaves at different foliar ages.

1 Introduction

The jujube tree is considered as one of the oldest recognized plants used for medicinal purposes, belonging to the Rhamnodaceae family. Jujube trees have strong adaptability to temperature and humidity and were widely spread in subtropical and tropical regions [1,2]. There were more than 700 varieties of jujube trees in China. It is the largest producer of jujube fruit globally, and the annual output of jujube fruit is about 4.5 million tons, accounting for almost 90% of the world’s total output [3,4]. More than 200 compounds have been found in jujube fruit, including triterpenes, flavonoids, glycosides, saponins, alkaloids, nucleosides, and glycosides [5,6,7,8]. It has rich nutrition and health care value and can be directly eaten or used as food additives and flavorings [9]. The jujube tree is full of treasures. Many parts of the jujube tree can be used as medicine. There is a long history of medicine in my country. Jujube fruit, jujube bark, jujube root, and jujube leaves can be used as medicinal materials. Similarly, the fruit, seeds, and leaves of the jujube tree have been widely utilized in traditional medicine to relieve diseases such as anemia, insomnia, palpitation, diarrhea, fever, and spleen deficiency [10]. Currently, the research on jujube leaves is gradually developing. Jujube leaves can be made into black or green tea by various processing techniques. The wild black tea with jujube leaves has helps in sleeping, nourishes the heart, acts as diuretic, and lowers the blood pressure [11]. Studies have shown that jujube leaves were enriched in many compounds, especially for the bio-active compounds such as saponins, flavonoids, and triterpene acids [11,12,13,14], which have several physiological as well as pharmacological functions, such as antioxidant and anti-inflammatory [12,14,15]. It has been reported that saponins that was isolated from leaves (fresh) of jujube can bind and remove potent risk elements, including cholesterol in the human blood [14]. The aqueous ethanolic leave extracts of jujube is used for treating liver cirrhosis and wound healing in animal experiments [16,17], and green tea extracts of jujube leaves have been used to prevent the development and growth of liver cancer cells [18]. In addition, the ethanolic aqueous extract from wild jujube leaves also demonstrated significant inhibitory effects on the central nervous system [19].

Given the essential biological functions of jujube leaves, it is very crucial to study the chemical components of jujube leaves grown at different foliar ages and systematically analyze the content and types of various compounds in them. Although some achievements about the chemical components in jujube leaves were obtained, few studies on the changes of chemical composition content and species in jujube leaves over time were conducted. In addition, due to the different growth environments, climate, and growth stage, the contents of active ingredients in jujube leaves are also different.

In this article, the relationship between the metabolites of jujube leaves and the foliar ages was analyzed and studied. Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) technology, combined with broadly targeted metabolomics [20], jujube leaves of three different foliar ages were selected for chemical composition analyses, as well as the relationship between the foliar ages and the chemical composition. The principal component analysis (PCA) was utilized to preliminarily distinguish the chemical composition of jujube leaves growing at different foliar ages. The chemical constituents of jujube plant leaves at various foliar ages were discriminantly analyzed through orthogonal partial least square-discriminate analysis (OPLS-DA). Similarly, the composition and the content distribution of compounds at three different foliar ages were analyzed by the hierarchical cluster analysis, and the critical chemical components were screened. This article identified the characteristic metabolomics of jujube leaves in different foliar ages, which has a strong guiding significance for the growth period selection of jujube leaves, and places a theoretical ground for the jujube leave applications.

2 Materials and methods

2.1 Materials and chemicals

The leaves of Chinese jujube (Ziziphus jujuba Mill.) were cultivated in the resource garden of the University of Luoyang Normal (Luoyang, Henan, China), and the materials used in this study were collected from jujube that is 6 years old at the end of May 2021. The three foliar ages, LS1 (one bud and two leaves), LS2 (one bud and three leaves), and LS3 (one bud and four leaves) (Figure 1), were obtained in 20, 27, and 35 days after germination, respectively. Methanol and acetonitrile (chromatographic grade) were purchased from Meker Company, Germany.

Figure 1 
                  Three different foliar ages of jujube leaves. LS1 and LS2 represented one bud with two leaves and there leaves, respectively, and LS3 represented one bud with four leaves. Developmental stages, LS1–LS3, corresponded to days of 20, 27, and 35 after germination, respectively. Bar size = 1 cm.
Figure 1

Three different foliar ages of jujube leaves. LS1 and LS2 represented one bud with two leaves and there leaves, respectively, and LS3 represented one bud with four leaves. Developmental stages, LS1–LS3, corresponded to days of 20, 27, and 35 after germination, respectively. Bar size = 1 cm.

2.2 Sample collection and treatment

To make the experimental data more representative, these flash-frozen samples were placed in containers containing liquid nitrogen, and then they were kept at 80°C for further examination. Three biological replicates of each foliar age were analyzed independently. For each biological replicates, five individuals were gathered and pooled. About 30 g jujube leaves were collected in LS1, LS2, and LS3, respectively, and then the leaves were washed with clean water and then air dried. Subsequently, the sample was crushed with a zirconia bead for about 1.5 min at the speed of 30 Hz in a mixer mill (MM 400, Retsch). The powder (100 mg) was finally extracted with 1.2 mL aqueous methanol (70%) and placed overnight at a temperature of 4°C. Before UHPLC-MS/MS analysis, the sample was centrifuged for 10 min at 12,000 rpm and then filtered with 0.22 m pore size (SCAA-104, Shanghai, China).

2.3 Conditions for UHPLC-ESI-MS/MS analyses

The extractive of Jujube leaves was investigated using a 6500 triple quadrupole-linear ion trap mass spectrometer (AB Sciex, Framingham, USA). In brief, the Waters ACQUITY UPLC HSS T3 C18 1.8 µm (2.1 mm × 100 mm) chromatographic column was used in this study; similarly, the mobile phase of solvent A was composed of acetic acid (0.04%) with pure water, and solvent B was composed of acetic acid solution with acetonitrile (0.04%). The elution gradient of the experiment was set as the follows: 95% A for 0–11 min followed by 5% A for 11–12 min and 95% A for 12–15 min. Flow velocity was set as 0.4 mL/min, and the column temperature was kept at 40°C. The injection volume of the sample was 2 μL.

The electrospray ionization source was used to ionize analytes. The following conditions were set for the interface: the 1.0 kV was set as capillary voltage; the cone voltage was 10 eV; the collision energy was 10 eV; the 150°C temperature was set for the ion source; the temperature of desolvation was 500°C; the gas flow of the cone was 50 L/h; and the desolvation gas flow was 600 L/h. The gas flow of the collision gas – N2 was 0.2 mL/min. The mass spectrometry scan was set to m/z 50–2,000. The daughter ion mode conducted monitoring at the collision voltage ranging from 10 to 40 eV.

2.4 Qualitative and quantitative analyses of the constituent compounds in jujube leaves

Both the MS data (primary and secondary) were utilized for metabolites annotations and qualitative analyses based on the public metabolite database and the self-built database MWDB (Metware Biotechnology Co., Ltd. Wuhan, China). Interference signals, such as the repeated signals of NH 4 + , Na+, and K+ ions, the repetitive signals of fragment ions, and the isotope signal were initially eliminated throughout analyses to assure the correctness of the metabolite annotations. The structure analysis (metabolite) was then carried out using the self-built database of MWDB and the existing public mass spectroscopy datasets, including KNAPSAcK (http://kanaya.naist.jp/KNApSAcK/), MassBank (http://www.massbank.jp/), MoToDB (http://www.ab.wur.nl/moto/), HMDB (http://www.hmdb.ca/), PubChem (https://pubchemblog.ncbi.nlm.nih.gov/), ChemBank (http://chembank.med.harvard.edu/compounds), METLIN (http://metlin.scripps.edu/index.php), and NIST Chemistry Webbook (http://webbook.nist.gov/). The Analyst software (version 1.6.3) was used to process metabolomics data (Sciex, Framingham, MA, USA).

The MRM mode of QQQ mass spectrometry was used to quantify the metabolites. To remove the interference in the MRM mode, the quadrupole was used to filter precursor ions of the target metabolite and eliminate the ions that correspond to other molecular weights. The peak area of the identified chemical substances from jujube leaves was calculated, and then the peak area of all the identified chemicals in leaves was integrated to compare and analyze the relative contents using MultiQuant (version 3.0.2, AB SCIEX, Concord, ON, Canada). Finally, the peak area of chromatographic was used to conclude the relative contents of the metabolites [21].

2.5 Widely targeted metabolomics analysis

The statistic function of prcomp in R was used to conduct an unsupervised PCA (www.r-project.org). Before unsupervised PCA, the data were unit variance scaled.

The results of Pearson correlation coefficients (PCCs) between samples were computed using the cor function in R (www.r-project.org) and displayed as heatmaps solely, while the hierarchical cluster analysis (HCA) of samples and metabolites were shown as heatmaps with dendrograms. The HCA and PCC were analyzed using the heatmap function in R package. The normalized signal intensities (unit variance scaling) of the metabolites were presented as a color spectrum for HCA. Further, for the identified metabolites, OPLS-DA was conducted. Fold change ≥2 or ≤0.5 and variable significance in the project (VIP) ≥1 were used to identify whether metabolites were differently accumulated across groups. VIP values were extracted from the OPLS-DA results, which included score plots and permutation plots, using the R package of MetaboAnalystR (https://github.com/xia-lab/MetaboAnalystR). Before conducting the OPLS-DA analysis, the data were log 2 converted and mean centered. A permutation test with 200 permutations was done to prevent overfitting.

The KEGG compound database (http://www.kegg.jp/kegg/compound/) was used to annotate the identified metabolites, and the annotated metabolites were then mapped to the KEGG pathway database (http://www.kegg.jp/kegg/pathway.html). The significance of pathways with substantially regulated metabolites was further evaluated by the hypergeometric tests with p-values <0.05, and the finally obtained metabolites were then input into metabolite set enrichment analysis.

2.6 Data analysis

The SPSS (version 3.0, IBM Corporation, Armonk, NY, USA) was used to conduct statistical analysis. The significant differences were determined using Duncan multiple range tests and one-way ANOVA. A p-value less than 0.05 was marked as significant. The OriginPro 2016 (Northampton, MA, USA) was adopted for the figure plotting.

3 Results and discussion

3.1 Metabolism analysis of jujube leaves during different foliar ages

The total ion current diagrams of chemical components of jujube leaves in three foliar ages were analyzed (Figure 2a), and 798 chemical substances were identified from the three foliar ages. These substances were divided into 21 categories, including 24 cabisans, 114 organic acids and their derivatives, 23 carbohydrates, 40 flavonols, 107 flavones, 69 lipids, 17 flavanones, 19 vitamins and their derivatives, 16 alcohols, 37 alkaloids, 68 phenylpropanoids, 91 amino acids along with its derivatives, 52 nucleotides along with its derivatives, 16 anthocyanins, 8 indoles and their derivatives, 20 polyphenols, 29 terpenoids, 7 isoflavones, 6 steroids, 3 quinones, and 32 other chemical substances. The metabolic substances of the leaves varied based on the foliar ages (Table S1). Among the metabolic substances of jujube leaves in different foliar ages, there were mainly lipids, flavones, flavonols, organic acids along with its derivatives, amino acids and their derivatives, nucleotides along with their derivatives, and phenolamine compounds. Specifically, in all the three foliar ages (LS1, LS2, and LS3), the higher metabolites were organic acids and their derivatives, followed by flavones and amino acids and their derivatives (Figure 2b–d). There were 99, 113, and 112 organic acids and their derivatives in LS1, LS2, and LS3, respectively (Figure 2b–d). For flavones, 98, 97, and 90 flavones were identified in three foliar ages, respectively (Figure 2b–d). Finally, 89, 89, and 84 amino acids and their derivatives were screened out in the three foliar ages, respectively (Figure 2b–d).

Figure 2 
                  Total ion chromatogram of metabolites (a) and the identified metabolites from the LS1 (b), LS2 (c), and LS3 (d) at three different foliar ages of jujube leaves.
Figure 2

Total ion chromatogram of metabolites (a) and the identified metabolites from the LS1 (b), LS2 (c), and LS3 (d) at three different foliar ages of jujube leaves.

3.2 PCA of the metabolic substances in jujube leaves

PCA is an effective tool that can be used to reduce dimensionality of the original complex data, which is also well known for its extensive data matrix. It extracts several principal components according to the multivariate data to verify the possible variability and then helps find the most significant information in the data [22,23,24]. In this study, PCA was performed using the total peak areas of metabolites, and the peak areas of all compounds identified in the jujube leaves at different foliar ages were used to distinguish the differences in different growth stages. Based on the PCA model shown in Figure 3, jujube leaves at three different foliar ages were clustered into LS1, LS2, and LS3, respectively, which were consistent with the reality. PC1 explained 46.8% of the total variables. LS1 and LS2 were located on the semi-axis (positive) of the X-axis and the LS3 was on the negative semi-axis of the X-axis. PC1 could distinguish leaves at different times in three foliar ages. PC2 explained 22.5% of the total variables. The LS1 was located on the positive semi-axis of the Y-axis, while LS2 and LS3 were positioned on the semi-axis (negative) of the Y-axis. Thus, it was concluded that the content differences of the components and metabolites in the LS1 and LS3 were smaller than those in the comparisons of LS1 vs LS2 and LS3 vs LS2. The PCA results of samples at three different foliar ages showed that on the score figures of the first and second principal components, the chemical information of jujube leaves at three different foliar ages showed significant separation features. Significant differences were found in the metabolite compositions in jujube leaves at different foliar ages, which were similar to the findings of Hao et al. [25], who studied the nutrient changes of persimmon leaves at different developmental stages in different varieties. They showed that the nutrient of persimmon leaves varied significantly in different varieties and at various developmental stages. There were also obvious differences in the physical and chemical compositions as well as the sensory quality of persimmon leaves at various developmental stages.

Figure 3 
                  Principal components of metabolites in jujube leaves at different foliar ages.
Figure 3

Principal components of metabolites in jujube leaves at different foliar ages.

3.3 Metabolites screening in jujube leaves in different foliar ages

After combining the VIP value and the fold change value (FC), the differential metabolites in foliar ages were further screened. To detect the significant change in metabolites at three foliar ages, metabolites with foldchange ≥2 or foldchange ≤0.5 and VIP ≥1 were nominated as the differential metabolites. Specifically, foldchange ≥2 or foldchange ≤0.5 indicates that the content differences of the metabolites between the control and experiment groups were more than two times or 0.5 times lower, respectively; foldchange ≥2 indicates the metabolite exhibiting the increasing tendency, and foldchange ≤0.5 indicates the decreasing tendency. The VIP value indicates the influence degree of the difference among the corresponding metabolite groups on the sample discrimination and classification of each group in the model; when VIP ≥1, the difference between metabolites is significant [26]. Based on the VIP and fold change values, volcano plots of differential metabolites were constructed (Figure 4).

Figure 4 
                  Volcano maps of the differential metabolites in the comparisons of LS1 vs LS2 (a) and LS2 vs LS3 (b).
Figure 4

Volcano maps of the differential metabolites in the comparisons of LS1 vs LS2 (a) and LS2 vs LS3 (b).

In this study, 66 differential metabolites were identified by comparing LS1 and LS2 (Figure 4a). Thirty-eight were decreased, while 28 were increased. Among the 28 increased metabolites, there were 9 flavones, 7 flavonols, 2 alkaloids, 3 anthocyanins, 1 organic acid along with their derivatives, 1 isoflavone and its derivatives, 1 carbohydrate, 1 flavanone and its derivatives, 1 nucleotide and its derivatives, 1 cabisan, and others. Among the 38 that decreased, there were 9 phenylpropanoids, 7 cabisans, 7 lipids, 3 carbohydrates, 3 organic acids along with its derivatives, 4 flavones, 2 amino acids along with its derivatives, and 1 vitamin along with its derivatives (Table S2).

Moreover, 104 differential metabolites were identified by comparing LS2 and LS3 (Figure 4b). Seventy-four metabolites were increased, and 30 metabolites were decreased. Among the 74 metabolites that increased, there were 13 flavones, 9 nucleotide and its derivatives, 8 lipids, 7 amino acid and its derivatives, 6 cabisan, 5 flavonols, 9 phenylpropanoids, 3 flavanone and its derivatives, 4 organic acid and its derivatives, 4 glycosides, 3 alkaloids, 1 vitamin and its derivatives, and 2 others (Table S3). The 30 metabolites that decreased are as follows: 5 organic acid and its derivatives, 6 flavones, 3 anthocyanins, 2 vitamin and its derivatives, 2 phenylpropanoids, 2 polyphenols, 1 proanthocyanidins, 2 flavonols, 1 nucleotide and its derivatives, 1 carbohydrate, 1 alcohol, 1 terpenoid, 1 alkaloid, and 2 others. Collectively, from the results described earlier, it could be concluded that the changes in metabolites between LS1 and LS2 were smaller than that in LS2 and LS3.

3.4 Hierarchical cluster analysis of differential metabolites in jujube leaves at different foliar ages

The important differential metabolites (124) were screened in the LS1, LS2, and LS3. HCA was then conducted on these 124 differential metabolites [27]; the results are shown in Figure 5. The content distributions of the 124 key differential metabolites had certain features in the LS1, LS2, and LS3. The metabolites with relative high contents in LS1 and LS2 were mostly overlapped. The change of differential metabolites between S1 and S2 is small, while the metabolites with relative high contents in the LS3 were obviously different from those in LS1 and LS2. Besides, the metabolites with relative high contents in the LS3 were lower than those in the LS1 and LS2, and the overlap degree of LS3 with LS1 and LS2 was significantly lower than that between LS1 and LS2.

Figure 5 
                  Heat map of the differential metabolites at three different foliar ages.
Figure 5

Heat map of the differential metabolites at three different foliar ages.

Due to the differences in the foliar ages, the metabolites also showed certain differences among the samples in LS1, LS2, and LS3. In the LS1, delphinidin, N-hydroxy tryptamine, serotonin, methylquercetin O-hexoside, tricin 7-O-hexoside, and eriodictyol C-hexoside were obviously higher than those in LS2 and LS3, and they were identified as the distinctive compounds in LS1. In the LS2, N-caffeoyl agmatine, lysoPC 18:3 (2n isomer), N-(4′-O-glycosyl)-p-coumaroyl agmatine, dihydromyricetin, and hydroxy-methoxycinnamate were higher than those in LS1 and LS3, and they were identified as the distinctive compounds in LS2. However, the relative high contents of metabolites in LS3 was obviously less than LS1 and LS2, and the distribution was significantly different from LS1 and LS2. The O-feruloyl 4-hydroxylcoumarin, 3-O-p-coumaroyl quinic acid, isorhamnetin 3-O-neohesperidoside, quercetin O-acetylhexoside, cyanidin 3-O-galactoside, and DIMBOA glucoside were identified as the distinctive compounds in LS3. Collectively, these characteristic compounds might provide a theoretical basis for the rapid identification of jujube leaves at different foliar ages.

4 Conclusion

The leaves of the jujube at three different foliar ages, LS1 (one bud and two leaves), LS2 (one bud and three leaves), and LS3 (one bud and four leaves), were investigated by the UHPLC-MS technique based on metabolomics. A total of 22 substance categories were identified, and the PCA indicated the significant variations of metabolites at the three foliar ages. The distinctive compounds of the three different foliar ages were identified by the screening of differential metabolites and HCA. In this study, the metabolomics method was used for the first time to analyze the components at different foliar ages of jujube leaves. The content changes of jujube leaves at different foliar ages were studied, which laid the theoretical foundation for the application of jujube leaves.


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  1. Funding information: This study was funded by the Chinese National Natural Science Foundation (with Grant Nos.: 32001669, 32101565) and Colleges and Universities of Henan Province Scientific Research Projects (with Grant No.: 20B180005).

  2. Author contributions: Hongxia Liu: funding acquisition, conceptualization, investigation, writing, and reviewing; Benliang Deng and Mingyue Xu: project administration, investigation, visualization, resources management; Lefei Wang and Hui Liu: formal analysis, validation, editing and writing, and reviewing; Xusheng Zhao: supervision of the entire experiment.

  3. Conflict of interest: All the authors in the study have declared that they have no conflicts of interest for the word or data published in this paper.

  4. Ethical approval: The conducted research is not related to either human or animal use.

  5. Data availability statement: The data gathered for this study has been analyzed and included in the manuscript. Furthermore, these data are also available on reasonable demand or request.

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Received: 2022-08-25
Revised: 2022-10-27
Accepted: 2022-10-30
Published Online: 2022-12-06

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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