Analysis of metabolites in young and mature Docynia delavayi (Franch.) Schneid leaves using UPLC-ESI-MS/MS

Introduction

Docynia delavayi (Franch.) Schneid is an evergreen plant belonging to the Rosaceae family, that is widely distributed throughout southwest China (Wu, 1987). Its fruits, leaves and barks contain extensive benefits in terms of nutrients and are popular for its antioxidant activity and extremely high-quality in medicinal uses (Bao, 2001). Currently, D. delavayi has been made into a variety of foods, such as dried fruits, fruit wine, juice, and vinegar (Han & Ren, 2014). In the local ethnic groups of southwestern China, Docynia leaves are widely used as a medicine for fever, cancer, empyrosis, and rheumatism (Deng et al., 2014). It has been reported (Liu et al., 2014b) that the leaves of D. delavayi are rich in polyphenols and the content of polyphenol is 2-feature that of apple leaves. Moreover, D. delavayi has considerable leaf biomass, thus, the D. delavayi leaves have great potential for utilization. So far, the study of D. delavayi mainly involves the conventional breeding, extraction of active ingredients and functional analysis of secondary metabolites (Li, Li & Li, 2010; Liu et al., 2014a; Tai, 2006; Zhao et al., 2012).

Metabolomics is the quantitative and qualitative study of low molecular weight metabolites (Xu et al. 2020). Metabolites are the basic of biological models and enable us to gain a visual and effective understanding of biological processes and mechanisms (Chu et al., 2020). Metabonomics strategies can reveal the relationship between metabolic profiles and recessive phenotypic traits (Han et al., 2015). Of the three sequencing technologies in metabolomics, widely targeted metabolomics integrates the advantages of both targeted and non-targeted metabolomics (Sun, 2019). For the past few years, UPLC-ESI-MS (Chen et al., 2013) has been widely used as a popular technique for the analysis and identification of plant metabolites because it saves time, uses less solvent, and provides an accurate compound determination (Li et al., 2019; Wang et al., 2019a; Yang et al., 2020; Zhu, Yin & Yang, 2020; Zou et al., 2020).

At present, the research on the D. delavayi is still in its initial stage, especially the leaves, which have not been studied in depth. At the same time, the leaves of D. delavayi have a high medicinal value, so it is important to comprehensive investigation of the metabolites in D. delavayi leaves. To better understand the metabolic differences between the young and mature leaves of D. delavayi, we performed UPLC-ESI-MS/MS analysis to identify and quantify the metabolites of young and mature leaves of D. delavayi. The results of this study reveal the metabolic changes in the young and mature leaves of D. delavayi and afford vital a theory basis for the utilization of D. delavayi leaves.

Material and methods

Results

Metabolic profiling

The total ion flow current (TIC) of the mass spectra obtained from the mass control samples in positive and negative ion detection modes respectively, as detected by the UPLC-MS/MS technique, were shown in Fig. S1. The high overlap of the spectra indicates that the detection method has good signal stability and reliable data results.

The metabolites of the young and mature leaves of D. delavayi were studied on the basis of UPLC-ESI-MS/MS and databases. Results showed that 477 metabolites were identified. The various metabolites were classified into different categories, including 111 flavonoids, 71 phenolic acids, 61 lipids, 52 amino acids and derivatives, 47 others (consisted of 9 vitamin, 18 saccharides and alcohols, and 20 unassigned metabolites), 33 nucleotides and derivatives, 30 organic acids, 24 terpenoids, 18 lignans and coumarins, 18 alkaloids and 12 tannins, as detailed in Table 1, Table S1. The analysis of the composition showed the highest content of flavonoids, accounting for 23.27% of the total metabolites.

Table 1:

Classification of compounds in young and mature leaves of D.delavayi.

NO.	Metabolite class	Quantity	percentage
1	Flavonoids	111	23.27%
2	Phenolic acids	71	14.88%
3	Lipids	61	12.79%
4	Amino acids and derivatives	52	10.90%
5	Others	47	9.85%
6	Nucleotides and derivatives	33	6.92%
7	Organic acids	30	6.29%
8	Terpenoids	24	5.03%
9	Lignans and Coumarins	18	3.77%
10	Alkaloids	18	3.77%
11	Tannins	12	2.52%
	total	477	100.00%

DOI: 10.7717/peerj.12844/table-1

In the clustering heat map (Fig. 1), it was shown by the expression levels of all metabolites that most of them were different in young and mature leaves. Compared with young leaves, most of the flavonoids and phenolic acids in the mature leaves were up-regulated. The terpenoids, tannins, and lignans and coumarins contents in mature leaves were higher than those in mature leaves. However, most of the alkaloids, others, amino acids and derivatives, organic acids and nucleotides and derivatives were down-regulated in expression in young leaves. This finding showed that D. delavayi mature leaves were clearly distinguished from the young leaves.

The PCA and OPLS-DA of young and mature leaves

In this study, we used PCA to perform multivariate statistical analysis of metabolites from six samples and extracted two principal components, PC1 and PC2, respectively, which had the cumulative contribution rates reached 92.62%. The PCA score plot show a clear metabolic difference between young and mature leaves, and the biological replicates were closely together (Fig. 2), suggesting that the experiment is reproducible and reliable.

In this research, samples were compared using the OPLS-DA model to evaluate the differences between Y (young leaves) and O (mature leaves). The results were R2X = 0.881, R2Y = 1, Q2 = 0.998 (Fig. 3). The Q2 values for Y and O were 0.998, which is greater than 0.9 indicating that the model was excellent and could be used for further analysis.

Differential metabolites screening

To better investigate the metabolic differences between young and mature leaves of D. delavayi by metabolomic analysis. We screened for differential metabolites based on VIP ≥1 and FC ≥ 2 or ≤ 0.5. The significant differential metabolites among young and mature leaves are listed in Table S2. The results of differential metabolite screening for young and mature leaves were illustrated using a volcano plot (Fig. 4). Concisely, there were 281 significantly different metabolites (135 down-regulated, 146 up-regulated), the rest were no-change metabolites. These significantly different metabolites between young and mature leaves were classified into 11 different categories, including 79 flavonoid (55 up-regulated, 24 down-regulated), 38 phenolic acids (16 down-regulated,22 up-regulated), 31 amino acids and derivatives(25 down-regulated, 6 up-regulated), 19 nucleotides and derivatives(16 down-regulated, 3 up-regulated), 16 lignans and coumarins(1 down-regulated, 15 up-regulated), 25 others (15 down-regulated,10 up-regulated), 11 alkaloids (7down-regulated,4 up-regulated),12 organic acids(10 down-regulated,2 up-regulated), 25 lipids (21 down-regulated,4 up-regulated), 10 tannins and 15 terpenoids (all up-regulated).

Based on differential metabolite analysis of young and mature leaves, a total of 12 differential metabolites could only be detected in mature leaves. The 12 mature leaves specific-metabolites were myricetin 3-o-galactoside, catechin-(7,8-bc)-4 β-(3,4-dihydroxyphenyl)-dihydro-2-(3H)-pyranone, myricetin-3-o-(6””-malony)glucoside”, feruloylmalic acid, hesperetin O-malonylhexoside, kaempferol-3-o-rhamnoside (afzelin)(kaempferin), glutathione reduced form, feruloylsinapoyltartaric acid, aminopurine, epicatechin-epiafzelechin, apigenin-c-rhamnoside, 3 β,19 α-dihydroxyolean-12-en-28-oic acid. Syringin, n-acetyl-DL-tryptophan, quillaic acid, 4-methyl-5-thiazoleethanol, ligustilide only exist in young leaves.

KEGG annotation and enrichment analysis

In the present study, we mapped the 90 differential metabolites to the KEGG database. First, we focus on the information about metabolic pathways, and we found that most metabolites are mapped to “metabolites”. A few metabolites belong to other system information categories, such as “Genetic Information Processing” and “Environmental Information Processing”. Results of the above annotation are enriched according to the pathway types in KEGG, and the enrichment results are shown in the bubble plot in Fig. 5. A total of 68 pathways were involved in the analysis of differential metabolites between young and mature leaves, among which the top four pathways in terms of the p-value for metabolic pathway enrichment analysis were “Flavonoid biosynthesis”, “Cysteine and methionine metabolism”, “Phenylpropanoid biosynthesis” and “C5-Branched dibasic acid metabolism”. Among these metabolic pathways, phenylpropanoid biosynthesis and flavonoid biosynthesis contained more differential metabolites than other metabolic pathways (9 and 10, respectively).

Discussion

In recent years, a widely targeted metabolomics method based on UPLC-ESI-MS/MS and multiple reaction monitoring (MRM) has been established as the technology has evolved. Compared to targeted metabolite detection methods, UPLC-ESI-MS/MS is characterized by high resolution, accurate characterization, short analysis times and high peak separation capacity (Farag & Shakour, 2019). For these advantages, UPLC-ESI-MS/MS is widely used for the metabolomic analyses in various plant species (Jeszka-Skowron, Zgoła-Grześkowiak & Frankowski, 2018; Matsuda et al., 2012). At present, the utilization of D. delavayi is mainly limited to its fruits, which are made into various food products, and the research on leaves is less, limited to the extraction of antioxidant components and determination of polyphenol content (Liu et al., 2014a; Liu et al., 2014b). Thus, this study aimed to identify the different metabolites in young and mature D. delavayi leaves, and the results could provide the theoretical basis for the subsequent production and utilization of D. delavayi leaves.

A total of 281 differential metabolites were screened by FC and VIP values. These differential metabolites best represent the differences between young and mature D. delavayi leaves and could provide a reference for future studies. Analysis of the differentially accumulated metabolites between the young and mature leaves showed that flavonoids are the main metabolites, of which 55 compounds were upregulated in mature leaves. It is being increasingly believed that flavonoids in leaves can promote the physiological survival of plants and protect them from fungal pathogens and UV-B radiation (Harborne & Baxter, 1999; Harborne & Williams, 2000). A previous study reported that flavonoids may function as antioxidants in response to excessive light exposure (Tattini et al., 2004). During D. delavayi growth, mature leaves have a longer developmental cycle than young leaves and receive external environmental stresses such as light for a longer period of time, which may be one of the reasons why most flavonoids accumulate higher in mature leaves than in young. The mature leaves of D. delavayi were more leathery and thicker than young, which may be related to the polyphenolic compound gallic acid in the plant (Getachew et al., 2009; Zhang et al., 2011). Studies have shown that gallic acid greatly contributes to leaf thickness and leatheriness (Kohorn & Kohorn, 2012; Rose & Lee, 2010; Wang et al., 2019b; Zhang et al., 2011).

In the metabolites of young and mature D. delavayi leaves, the content of cinchonain Ic, syringic sldehyde-glucoside, and L-ascorbic acid were 103, 64, and 50 times higher in mature leaves than in young leaves. We identified cinchonain Ic, syringic sldehyde-glucoside as polyphenols, which are the main antioxidant and antitumor active substances in vegetables and fruits (Gharras, 2009; Manach et al., 2004), and L-ascorbic acid as an antioxidant can synergistic effect with polyphenols to improve antioxidant efficacy (Gharras, 2009). We also detected four flavonoids (quercetin, chrysin, avicularin, and naringenin) present in D. delavayi leaves, which is consistent with previous studies on D. delavayi rhizomes (Deng et al., 2014), and all of these compounds have some antitumor activity. It could be useful for future functional and nutritional assessments of D. delavayi leaves. In our study, both young and mature leaves having their own unique metabolites. Differential metabolite analysis showed that most of the 12 metabolites present only in mature leaves have anti-tumor, anti-cancer, antioxidant, anti-inflammatory, anti-osteoporotic, anti-allergy, and other effects, which have high development value (Bernatova, 2018; Chen et al., 2020; García-Lafuente et al., 2009; He et al., 2020; Ke et al., 2019; Kim et al., 2004; Li et al., 2017; Wei et al., 2019; Li et al., 2019).

We also found an interesting phenomenon that terpenoids and tannins were significantly upregulated in mature leaves. Terpenoids are usually produced in vegetative tissues, flowers, and, occasionally, roots (Dudareva, Pichersky & Gershenzon, 2004). In D. delavayi leaves, terpenoids are mostly triterpenoids, which have the functions of resist inflammation, and inhibit the proliferation of tumor cells (Banno et al., 2005; Rufino-Palomares et al., 2013). This is similar to the previous function of specific metabolites in mature leaves, which further confirms that the content of anti-inflammatory and anti-tumour compounds is higher in mature leaves than in young. Tannins are an important bioactive class of compounds with potential antioxidant and antibacterial activities, as well as good anti-inflammatory and wound healing potential (Ambreen & Mirza, 2020; Dos Santos et al., 2017; Ogawa & Yazaki, 2018). Tannins increased as the leaves grow, all tannin compounds are upregulated in mature leaves. Reports by Hyder et al. (2002) reveal that compared to mature leaves, immature leaves of plants contained high levels of tannins, especially in stressed conditions. This is not consistent with our results and may be due to a defense mechanism that plants acquire when trying to protect themselves from excessive leaf shedding (Toit, Sithole & Vorster, 2020).

Further study of the KEGG enrichment analysis showed that the most significant of phenylpropanoid biosynthesis was found in the comparison groups between young and mature leaves. A total of nine metabolites were enriched to this pathway and their structures metabolites are shown in Fig. S2, with most of the differential metabolites (l-phenylalanine, caffeic acid, ferulic acid, coniferyl alcohol, syringin, sinapaldehyde) being down-regulated in mature leaves (Fig. S3). This may be due to the increase in secondary metabolites such as phenolic acids that were critical for environmental adaptation and plant survival during early nutrition (Aidoo et al., 2016; Zhang et al., 2017).

Conclusions

In the present study, we successfully performed UPLC-ESI-MS/MS-based metabolic analysis to compare metabolites of young and mature leaves of D. delavayi in a systematic method. A total of 477 metabolites were detected, 281 of which were differential metabolites. Differential metabolite analysis showed that flavonoids were the predominant metabolites, with most flavonoids, terpenoids and tannins being up-regulated in mature leaves. In addition, we identified 12 compounds present only in mature leaves, all of which have some antioxidant, anti-inflammatory and anti-cancer effects. Overall, this study contributes to the understanding the composition of D. delavayi leaves metabolites and also provides a reference for the future medical use and development of D. delavayi leaves.

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