Analysis of Bioactive Components in the Fruit, Roots, and Leaves of Alpinia oxyphylla by UPLC-MS/MS

Alpinia oxyphylla (A. oxyphylla) fruit has long been used in traditional Chinese medicine. In our study, the bioactive components of its roots, fruit, and leaves were investigated, and their potential medical value was predicted. The root, fruit, and leaf samples were analyzed using a UPLC-MS/MS system. The mass spectrometry outcomes were annotated by MULTIAQUANT. The “compound-disease targets” were used to construct a pharmacology network. A total of 293, 277, and 251 components were identified in the roots, fruit, and leaves, respectively. The fruit of A. oxyphylla had a higher abundance of flavonols. The roots of A. oxyphylla were enriched in flavonols and phenolic acids. The leaves of A. oxyphylla exhibited high contents of flavonols, phenolic acids, and tannins. Furthermore, network pharmacology analysis showed that flavonoids are the most important effectors in the fruit of A. oxyphylla and phenolic acids are the most important effectors in the roots and leaves. Moreover, the results suggested that the tissues of A. oxyphylla might play a role in the regulation of disease-related genes. The whole plant of A. oxyphylla is rich in natural drug components, and each tissue has high medicinal value. Therefore, comprehensive utilization of A. oxyphylla can greatly improve its economic value.


Introduction
Alpinia oxyphylla (A. oxyphylla) is commonly used in traditional Chinese medicine (TCM). e dried, ripe fruit of A. oxyphylla has long been used for treating diarrhea, enuresis, dementia, and other disorders [1]. Modern pharmacological studies have shown that A. oxyphylla extracts have antioxidant and anti-inflammatory capacities [2,3]. In addition, A. oxyphylla has been used for the treatment of diabetes [3] and Alzheimer's disease [4].
ese results highlight that A. oxyphylla fruit has a variety of drug components, and it is still meaningful to comprehensively determine the chemical components of A. oxyphylla tissues.
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) provides a vital tool to systematically analyze TCM metabolites [11]. Two flavonoids (chrysin and tectochrysin) from A. oxyphylla fruit extract were determined by LC-MS/MS with a method exhibiting accuracy ranging from −8.8% to 7.5% [5]. Li et al. identified nine compounds from A. oxyphylla fruit, which was achieved with 70% ethanol [11]. Moreover, Chen et al. detected the differential secondary metabolites of seed and fruit capsules by LC-MS/MS [12]. erefore, technical advances in the large-scale analysis of metabolites have helped to reveal the complex processes associated with modulating plant metabolism.
Among the plants of the genus Alpinia, the fruit or roots of plants are used as a medicine. Traditionally, the roots and fruit of Alpinia officinarum are used as medicines [13]. However, there are few systematic studies on the components of the roots and leaves of A. oxyphylla. As a result, the medicinal value of A. oxyphylla may be underestimated. erefore, the roots, fruit, and leaves of A. oxyphylla were collected, and untargeted metabolomics analysis was performed by LC-MS/MS. Furthermore, network pharmacology analysis can help us comprehensively understand the medicinal value of A. oxyphylla tissues.

Metabolite Extraction.
Fresh samples were freeze-dried under vacuum and then ground (30 Hz, 1.5 min) to powder with a grinder (mm 400, Retsch, Germany). One hundred milligrams of powder was dissolved in 1.0 mL of a 70% methanol aqueous solution. e dissolved sample was refrigerated overnight at 4°C three times. e samples were centrifuged at 10,000x g for 10 min at 4°C, and the supernatants were collected and then filtered with a microporous membrane filter (0.22-μm pore size). e prepared extracts were stored in sampler vials for LC-MS/MS analysis.
High-resolution MS/MS was used to detect metabolites eluted from the column. e electrospray ionization temperature was set at 550°C, and the MS voltage was set at 5500 V. e curtain gas was set at 25 psi. e collision-activated dissociation was set at high.
To compare the differences in the metabolites, the mass spectral peaks of each metabolite detected in different samples were corrected to ensure the accuracy of qualitative and quantitative analyses. Figure S1 shows the integral correction results of the quantitative analysis of randomly selected metabolites in different samples. e abscissa is the retention time (min) of the metabolite, and the ordinate is the ion current intensity of metabolite ion detection. e metabolites were quantified by the multiple reaction monitoring (MRM) mode of triple-quadrupole mass spectrometry [14]. Quality control samples were prepared by mixing sample extracts and analyzing the repeatability of samples by the same treatment methods. In the process of instrumental analysis, a quality control sample was analyzed every ten samples to monitor the repeatability of the UPLC-MS/MS system over the entire detection process.
After obtaining the mass spectrometric data of metabolites from different samples, the peak area of all mass spectral peaks was integrated, and the peaks of the same metabolite in different samples were integrated and corrected [14]. e mass spectrometry file of each sample was opened with MULTIAQUANT software, and the integration and correction of chromatographic peaks were conducted. e peak area of each chromatographic peak represents the relative levels of the corresponding substances.

Target Identification and Network Construction.
e target compounds were searched against the SWISSADME (http://www.swissadme.ch/) [17] and TargetNet (http:// targetnet.scbdd.com/calcnet/index/) databases [18], which are designed to identify potential target compounds via various prediction algorithms. Homo sapiens origin targets were used in the following analysis. Only targets with 95% possibility were included for the disease-related targets.
To compile the disease targets for susceptibility to atherosclerosis, Alzheimer's disease, liver disease, diabetes mellitus, allergies, Parkinson's disease, and depression, we searched the GeneCards database [19]. For each disease, duplicated targets were removed. e intersection between the drug and disease targets was determined to screen key targets.
e "compound-disease targets" were the intersection of A. oxyphylla compound targets and disease targets. e network was constructed and analyzed with the Cytoscape platform [20].

Systematic Correlativity Analysis and Statistical Analysis.
Pearson's correlation, one-way analysis of variance (ANOVA), and hierarchical (average linkage) clustering were conducted for the untargeted metabolism analyses. P-values of the ANOVA were adjusted for the false discovery rate. Principal component analysis (PCA) and partial least squares discrimination analysis (PLS-DA) of the metabolites were performed using SIMCA v14.0 (Umetrics, Umea, Sweden).

Untargeted Metabolite Profiling of the Metabolites in Different Tissues.
A total of 312 secondary metabolites were found by untargeted metabolomics analysis (Table S1), including phenolic acids, flavonols, tannins, lignans, coumarins, terpenoids, alkaloids, and quinones. PCA data showed three distinct sample groups, indicating that there was separation among the three tissues ( Figure 1(a)). As shown in Figure 1(b), the roots, fruit, and leaves contained 293, 277, and 251 metabolites, respectively. In total, the abundance of metabolites in the roots and fruit was not significantly different, while the abundance of metabolites in the leaves was approximately 51.41% of that in the fruit. All annotated metabolites were classified to identify the differentially accumulated metabolites between tissues (Figure 1(b)). In the roots, 111 flavonoids accounted for 47.40% of the total abundance, 97 phenolic acids accounted for 17.51%, and 15 terpenoids accounted for 13.70% (Figures 1(b) and 1(c)). Among the fruits, flavonoids were the most abundant, with 113 species in total, accounting for 58.86% of the total abundance. e abundance of phenolic acids ranked second, with 91 species, accounting for 14.09%. e abundance of tannins was the third highest, with 13 species, accounting for 5.00% (Figures 1(b) and 1(c)). In the leaves, the three compounds with the highest contents were flavonoids (90 species, accounting for 33.50%), phenolic acids (82 species, accounting for 20.08%), and tannic acids (12 species, accounting for 13.91%) (Figures 1(b) and 1(c)). Moreover, 116 metabolites predominantly accumulated in the roots, 120 metabolites were present at relatively high abundance in the fruits, and 76 metabolites were more highly distributed in the leaves (Figure 1(d)). erefore, the characteristics of metabolites in the fruit, roots, and leaves of A. oxyphylla were significantly different.

Variations in the Abundance Levels of Flavonoids among
Tissues. As shown in Figure 2, 115 flavonoids were identified in A. oxyphylla tissues. Heatmap clustering analysis found that more flavonoids accumulated in the fruit than in the roots and leaves (Figure 2(a)). e phenolic acids with the highest abundance in the fruit were prunetin, rhamnetin, and luteolin-7-O-glucuronide-5-O-rhamnoside. e 3 most abundant phenolic acids in the roots were delphinidin-3-O-(6''-O-p-coumaroyl) glucoside, hyperin, and quercetin-7-O-(6''-malonyl) glucoside. e phenolic acids with the highest abundance in the leaves were pinostrobin, epicatechin glucoside, and catechin-catechin-catechin.

Variations in the Abundance Levels of Phenolic Acids among Tissues. As shown in
en, phenolic acids were enriched in known synthetic pathways ( Figure 3(b)). Cinnamic acid, caffeic acid, vanillic acid, four coumaroyl derivatives, and three feruloyl derivatives were highly accumulated in the roots. p-Coumaric acid, p-coumaroyl, quinic acid, chlorogenic acid, coniferol, four coumaroyl derivatives, two feruloyl derivatives, and three sinapoyl derivatives were highly accumulated in the fruit. Ferulic acid, coniferol, sinapate, and sinapaldehyde were highly accumulated in the leaves. erefore, different phenolic acid synthetic strategies were employed in the fruit, roots, and leaves of A. oxyphylla.

Network Pharmacology Analysis Based on Major Components in Tissues.
In fruits, the 20 most abundant metabolites accounted for 52.90% of the total, of which 13 flavonoids accounted for 36.77% (Table 1). ese compounds were accepted as candidates to predict the targets.
e GeneCards database was used to predict the disease (cancer, osteoporosis, allergic disease, dementia, Parkinson's disease, kidney disease, diabetes mellitus, cardiovascular disease, and depression) targets. ree hundred fourteen overlapping genes were selected as potential targets for integrative network analysis. irteen flavonoids had 184 target genes, while phenolic acids had 44 targets. Network pharmacology analysis showed that flavonoids are the main effectors, which could interfere not only with cancer, cardiovascular disease, kidney diseases, and diabetes mellitus but also with depression ( Figure 4).

Evidence-Based Complementary and Alternative Medicine
Among them, phenolic acids may be involved in the regulation of 190 genes, flavonoids are associated with 126 genes, and terpenoids may target 34 genes. Network pharmacology analysis showed that phenolic acid is the main effective component of the roots, and it has the potential to interfere with diseases such as cancer, cardiovascular disease, kidney disease, diabetes, depression, dementia, and Parkinson's disease ( Figure 4).
In leaves, the top 20 metabolites accounted for 65.79%, and the highest contents were observed for flavonoids (6 species, accounting for 23.99%), tannins (5 species, accounting for 11.53%), and phenolic acid compounds (3 types, accounting for 7.86%) ( Table 3). Furthermore, 418 target genes were found, among which phenolic acids target 305 genes and flavonoids may affect the expression of 137 genes. erefore, phenolic acids may also be the main active      Figure 3: Accumulation of phenolic acids in the three tissues. (a) e heatmap scale ranges from −1 to +1 after data homogenization. (b) e biosynthetic pathway of phenolic acid. e green color text indicates that the relative concentration was higher in the roots than in the other tissues, the purple color text indicates that the relative concentration was higher in the fruit, and the yellow color text indicates that the relative concentration was higher in the leaves.   biosynthetic pathway of flavonoids. e green color text indicates that the relative concentration was higher in the roots than in the other tissues, the purple color text indicates that the relative concentration was higher in the fruit, and the yellow color text indicates that the relative concentration was higher in the leaves.
Evidence-Based Complementary and Alternative Medicine components in leaves ( Figure 4). erefore, the roots, leaves, and fruit of A. oxyphylla can be used as a candidate component source to intervene in multiple diseases.

Discussion
Multiple bioactive components have been separated from A. oxyphylla fruit [8]. In the present study, we analyzed the chemical components in the roots, leaves, and fruit of A. oxyphylla through UPLC-MS/MS. Based on the analysis results, the intervention effects of different tissues of A. oxyphylla on multiple diseases were predicted.
In this study, PCA showed that the components in fruits, roots, and leaves were quite different. e metabolome data were further analyzed by orthogonal partial least squares discriminant analysis (OPLS-DA), which further demonstrated the differences among the fruit, roots, and leaves [21]. Permutation verification of OPLS-DA (n � 200, 200 permutation experiments) showed that the R2' and Q2' were both smaller than the R2 and Q2 of the original model, and this model was meaningful ( Figure S2).
Previous studies have separated hundreds of essential oil components and 128 other types of components from the fruit of A. oxyphylla, including 81 terpenes, six diarylheptanoids, seven flavonoids, and five steroids [1]. ese studies used different methods to extract the fruit of A. oxyphylla and obtained a variety of components. In addition, due to the different production areas of A. oxyphylla, the components were also affected [22]. In this study, 70% methanol was used for extraction, and a total of 277 secondary metabolites were obtained in the fruit of A. oxyphylla. Some components have been reported in previous studies, such as kaempferol, while a number of components were highlighted here for the first time. Moreover, the relative abundance of each component was also quantified, which provided a basis for the functional prediction of A. oxyphylla fruit.
e isolated flavonoids included tectochrysin, izalpinin, kaempferide, kaempferol-7,4-dimethyl ether, chrysin, rhamnocitrin, and pinocembrin [7,23,24]. e isolated phenolic acids included protocatechuic acid, vanillic acid, 3,5-dihydroxy-4-methoxybenzoic acid, and isovanillin [25]. In the present study, the analysis results showed that the  Among phenolic acids, the abundance of sinapoyl derivatives, coumaroyl derivatives, and p-coumaric acid was   [26], provide neuroprotection in Alzheimer's disease [4], enhance kidney function [27], and induce cancer cell apoptosis [28]. Moreover, prunetin could induce cell death in gastric cancer cells, relax aortic rings, and promote bone regeneration [29][30][31]. Rhamnetin could play a role in inducing cancer cell apoptosis, inhibiting cell proliferation, and preventing cancer formation [32][33][34]. Rhamnetin has the potential to treat oxidative myocardial disease [35]. Pinostrobin may serve as a novel agent for lipid management, cancer treatment, and Parkinson's disease neuroprotection [36][37][38]. Pinocembrin is effective in treating ischemic stroke, and it also shows excellent neuroprotective potential [39]. Gingerenone A may be used as a potential therapeutic candidate for the treatment of obesity and diabetes [40,41]. Studies have shown that kaempferol has multiple bioactivities, such as antioxidant, neuroprotective, anticancer, anti-inflammatory, antidiabetic, and antiosteoporotic activities [42,43]. Phenolic acids have many unique functions, such as memory improvement, antioxidation, antidiabetic, anti-inflammation, and antiaging functions [44][45][46]. In the present study, the 20 most abundant components in fruit could dock to 314 genes, which were categorized into various pathways, such as respiratory electron transport, LPA receptor-mediated events, and the HIF-1-alpha transcription factor network (Table S2). Network pharmacology predictions showed that the components and targets were associated with cancer, cardiovascular disease, kidney diseases, diabetes mellitus, and depression. ese results match those observed in previous studies showing that A. oxyphylla fruit can play a role in the treatment of a variety of diseases. e roots of some Alpinia species, such as A. officinarum, are used for medicine [47]. e main components in the roots of A. officinarum were kaempferol, quercetin, diphenylheptane, and volatile oils [47]. A. officinarum is used to treat digestive disorders, stomachache, flatulence, and the common cold [47]. However, the medicinal value of A. oxyphylla roots is still mostly unknown. In the present study, a large number of secondary metabolites were detected in the roots. Flavonoids and phenolic acids accounted for 64.91% of the total abundance, and terpenoids accounted for 13.70%. erefore, flavonoids, phenolic acids, and terpenoids are the representative components of A. oxyphylla roots.
It has been reported that the physiological activities of quercetin include anticancer, hypoglycemic, and antiobesity activities [42,43,48]. Delphinidin has a variety of pharmacological activities, including anticancer, cardiovascular protection, neuroprotection, antidiabetes, and antiobesity activities [49]. Ferulic acid could offer beneficial effects, such as anticancer, antidiabetes, and antineurodegenerative effects [50]. Nootkatol could prevent UV-induced photoaging [51]. In the present study, delphinidin, kaempferol derivatives, and quercetin derivatives were the dominant flavonoid components in A. oxyphylla roots, while feruloylmalic acid and feruloyl derivatives were the dominant phenolic acid components. Among terpenoids, oxyphyllol D, oxyphyllol A, oxyphyllenone B, and nootkatol were present at higher levels. Moreover, the top 20 abundant components in the roots docked with 378 genes, which were related to 220 pathways, including lipid metabolism, inflammatory response, and neurotransmitter metabolism (Table S2). e target genes might be involved in multiple diseases, including cancer, cardiovascular disease, kidney diseases, diabetes mellitus, depression, dementia, and Parkinson's disease.
ese analysis results indicate that A. oxyphylla roots also have high medicinal value.
Few studies have analyzed the chemical constituents of volatile oil and organic acids from the leaves of A. oxyphylla [52]. Systematically analyzed chemical components of A. oxyphylla leaves have not been reported.
e present study demonstrated that the total abundance of metabolites in leaves was approximately 51.40% of that in the fruit, and the dominant components were flavonoids (33.50%), phenolic acids (20.08%), and tannins (13.91%). Pinostrobin, epicatechin, rhamnetin, pinocembrin, and prunetin were the most abundant flavonoids. Among the tannins, procyanidin C1, procyanidin B2, procyanidin B3, procyanidin B4, and procyanidin C2 had a high abundance. erefore, this study systematically analyzed the drug components of A. oxyphylla leaves and clarified the main chemical components of A. oxyphylla leaves.
Recent studies have shown that epicatechin plays a role in improving cardiovascular and cerebrovascular diseases and exerts anti-inflammatory, antidiabetic, and neuroprotective effects [53]. Procyanidin is considered to be involved in lipid regulation and cancer treatment [54,55]. In the present study, the top 20 abundant components in the leaves of A. oxyphylla docked with 416 genes, which were related to multiple pathways, including respiratory electron transport, IL1-mediated signaling events, and the TNF receptor signaling pathway (Table S2). Network pharmacology predictions showed that components in the leaves were also associated with a variety of diseases. Even its target genes had a higher relationship degree with the analyzed diseases than those of roots and fruits. A. oxyphylla leaves are readily available. us, the use of leaves as medicine can significantly increase the economic value of A. oxyphylla.
In summary, metabolic profiles revealed that the levels of metabolite accumulation might vary significantly among the fruit, roots, and leaves of A. oxyphylla. e representative components of A. oxyphylla fruit were flavonoids and phenolic acids. Flavonoids, phenolic acids, and terpenoids were the main components in A. oxyphylla roots. Flavonoids, phenolic acids, and tannins were the dominant components in A. oxyphylla leaves. Furthermore, the network pharmacology predictions suggest that the fruit, roots, and leaves of A. oxyphylla were associated with cancer, cardiovascular disease, kidney diseases, and diabetes mellitus. In addition, different tissues of A. oxyphylla could be used to treat more different diseases. erefore, further studies on the drug components and functions of tissues of A. oxyphylla will help to improve the medicinal value and economic value of A. oxyphylla.

Data Availability
All the datasets generated and analyzed during the current study were uploaded with the manuscript as additional files.

Conflicts of Interest
e authors declare that they have no conflicts of interest.

Authors' Contributions
Guankui Du contributed to conceptualization, review and editing of the paper, project administration, and funding acquisition; Deli Wang contributed to methodology; Li Ying performed to data curation and original draft preparation. All the authors have read and agreed to the published version of the manuscript.

Acknowledgments
is study was funded by the National Natural Science Foundation of China (no. 81960672) (Guankui Du). Figure S1: MRM metabolite detection. e multipeak diagram shows the substances that were detected in the sample, and each mass spectral peak with different colors represents one detected metabolite. Figure S2: OPLS-DA analysis model verification diagram. Table S1: 312 identified metabolites. Table S2: the target genes were enriched in multiple pathways. (Supplementary Materials)