Profiling of Widely Targeted Metabolomics for the Identification of Secondary Metabolites in Heartwood and Sapwood of the Red-Heart Chinese Fir (Cunninghamia Lanceolata)

The chemical composition of secondary metabolites is important for the quality control of wood products. In this study, the widely targeted metabolomics approach was used to analyze the metabolic profiles of heartwood and sapwood in the red-heart Chinese fir (Cunninghamia lanceolata), with an ultra-performance liquid chromatography-electrospray ionization tandem mass spectrometry system. A total of 224 secondary metabolites were detected in the heartwood and sapwood, and of these, flavonoids and phenolic acids accounted for 36% and 26% of the components, respectively. The main pathways appeared to be differentially activated, including those for the biosynthesis of phenylpropanoids and flavonoids. Moreover, we observed highly significant accumulation of naringenin chalcone, dihydrokaempferol, pinocembrin, hesperetin, and other important secondary metabolites in the flavonoid biosynthesis pathway. Our results provide insight into the flavonoid pathway associated with wood color formation in Chinese fir that will be useful for further breeding programs.


Introduction
The xylem of most woody plants can be divided into three parts: lighter sapwood (SW) on the periphery, darker heartwood (HW) on the inside, and the transition zone (TZ) at the color junction. HW contributes most to the value of the wood, and in most cases, it has a deep color that is likely associated with secondary metabolites. SW is often referred to as living tissue (5-25% of the components) and HW is mostly considered dead cells. The associated regulatory genes for metabolite production have been difficult to clarify in these tissues, particularly in HW. Some tree species have a TZ that produces secondary metabolites on a temporary basis, and includes live parenchyma cells; it is generally believed that this metabolic activity peaks rapidly in the TZ and produces a large number of secondary metabolites that accumulate in the HW [1].
Given the value of HW, some studies have specifically focused on its color. Miyamoto et al. [2] found that the moisture and potassium contents differed significantly between two groups of reddish and blackish HW in Sugi (Cryptomeria japonica). The color of HW in Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) significantly changes after heat treatment, which may be due to

Plant Materials
We used wood samples of 9-year-old red-heart Chinese fir belonging to clone cx746, collected from Xiaokeng State Forest Farm (Guangdong, China, 24 • 70 N, 113 • 81 E, 328-339 m above sea level). Three cores were collected from each individual tree from the main stems at breast height (1.3 m) using a tree growth cone and three random individuals were selected in clone cx746. The wood samples were split into two groups, based on color, and included SW in the outer wood, which was light yellow, and HW in the inner wood with a deep red color. All samples were flash-frozen in liquid nitrogen and maintained at −80 • C for further use.

Extraction and Separation of Flavonoid Secondary Metabolites
The three red-heart Chinese fir wood core samples were mixed and freeze-dried under a vacuum, ground into a fine powder in liquid nitrogen, mixed thoroughly, and then crushed for 1.5 min at 30 Hz using a mixer mill (MM 400, Retsch) with zirconia beads. The samples (100 mg, per sample) were extracted with 1 mL 70% methanol on a rotating wheel at 4 • C in the dark for 12 h. After centrifugation at 10,000 g for 10 min at 4 • C, the extracts were filtered (SCAA-104, 0.22 µm pore size; ANPEL) and then analyzed by LC-MS/MS (ThermoFisher Scientific, SanJose, CA, USA). Quality control samples were prepared by mixing all the samples equally. During the analyses, the quality control samples were run every 10 injections to monitor the stability of the analysis conditions.

Data Analyses
Principal component analysis (PCA) was employed to compress the original data into several principal components to describe the characteristics of the original data set [27]. The PC represents the combination of variables that explains most of the variance, in descending order. The main parameter obtained from PCA, R2X, represents the interpretation rate of the original data after dimensionality reduction and can be used to judge the quality of the model.
Partial least squares-discriminant analysis (PLS-DA) was used to help identify the differential metabolites. We also combined orthogonal signal correction (OSC) with PLS-DA being an OPLS-DA that further decomposes the X matrix into two types of information: related or unrelated to Y. OPLS-DA could filter differential variables by removing unrelated differences. The prediction parameters for evaluating the OPLS-DA model are R2X, R2Y, and Q2, where R2X and R2Y represent the interpretation Forests 2020, 11, 897 4 of 13 rate of the model built to the X and Y matrices, and Q2 represents the prediction ability of the model. The closer these three are to 1, the more stable the model. When Q2 > 0.5, the model can be considered effective, and when Q2 > 0.9, it can be considered excellent [28]. The OPLS-DA results enable a preliminary screening of differential metabolites between different tissues. In this study, a combination of the fold-change value and the VIP value was used to screen for differential metabolites. For the experiments, we used a completely randomized block design, and repeated the experiments three times. Microsoft Excel 2016 (Microsoft, Seattle, WA, USA) and IBM SPSS Statistics 24.0 (IBM Corporation, Armonk, NY, USA) were also used to evaluate the data assessed differential secondary metabolites. Differences in metabolites in HW and SW were evaluated using analysis of variance (ANOVA) with Duncan's multiple range tests for multiple comparisons. A p-value ≤ 0.05 for the ANOVA F-test was considered statistically significant.

Metabolic Profiling of Heartwood and Sapwood Based on LC-MS/MS
To investigate differences in the composition of secondary metabolites between the HW and SW of the red-heart Chinese fir, widely targeted metabolomics assay was performed, which analyzed the metabolic spectrum using UEMS. Metabolites were quantitatively analyzed following the collection of secondary data using the MRM model. Collectively, a total of 224 secondary metabolites were covered in our assay (HW and SW; Figure 1A), including 80 flavonoids, 58 phenolic acids, 14 alkaloids, 9 lignans and coumarins, 7 tannins, 4 terpenoids, and 52 others ( Figure 1B). Among these secondary metabolites, the most abundant were flavonoids.

PCA
To compare the secondary metabolites in the HW and SW, the dataset obtained from the LC-MS/MS in ESImode was subjected to PCA. In the PC1 × PC2 value plot ( Figure 2B), the samples were separated in the first principal component (PC1), which reached 60.01%. The model of PC1 and PC2 explained 79.04% of the variance in total and showed different metabolites between the heartwood and sapwood. The results showed a distinct grouping of HW and SW samples into two distinct areas in the plot, indicating that each cultivar had a relatively distinct metabolic profile. In addition, multivariate statistics were used to assess further the differences in metabolic profiles between HW and SW, and hierarchical cluster analysis (HCA) of the secondary metabolites in HW and SW was also performed. This showed two main clusters based on the relative differences in the accumulation patterns in the different tissues ( Figure 2C). The secondary metabolites belonging to cluster 1 accumulated at higher levels in SW and in contrast, the metabolites in cluster 2 accumulated at higher levels in HW. Taken together, the plot shows that there were significant differences in the secondary metabolites detected in HW and SW.

OPLS-DA
The OPLS-DA model was used to screen the differential compounds in both groups of samples and analyze metabolic differences between HW and SW ( Figure 3B). We observed high predictability (Q2) and strong goodness of fit (R2X, R2Y) between HW and SW (Q2Y = 0.998, R2X = 0.898, R2Y = 1.000). The HW was separated from SW, indicating major differences in the metabolic profiles between the two different colored tissues. Moreover, fold-change scores of ≥2 or ≤0.5 among the metabolites with a VIP value ≥1 was used to identify different metabolites. The screening results were analyzed using volcano plots ( Figure 3A). A total of 80 significant differences between metabolites were identified, of which 37 were significantly up-regulated (the relative content of heartwood is larger than that of sapwood) and 43 were significantly down-regulated in HW and SW, respectively. The top 10 metabolites that were significantly up-regulated in HW are listed in Table 1, and among them, 6 flavonoids and 2 phenolic acids were significantly different between the HW and SW. These metabolites were mainly flavonoids and they can be considered the representative differential metabolites of HW and SW, influencing the properties and particularly the color of the wood.

Putative Metabolic Pathways for Signal Metabolites
To elucidate the pathways of the differential metabolites, we mapped the metabolites from the HW and SW to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome. jp/kegg/). The results are shown in Figure 4A. These differential metabolites are mainly involved in metabolic pathways and flavonoid biosynthesis, and others, such as phenylpropanoids, flavone, and flavonol also showed differences. Flavonoids synthesized via the general phenylpropanoid pathway constitute a group of secondary metabolites in plants that contribute to the key characteristics of HW in woody plants, such as decay resistance and insect decay [29,30]. Further enrichment analyses of the main secondary metabolites showed that they were highly enriched in the flavonoid synthesis pathway, as shown in Figure 4B, and that flavonoids constituted the main secondary metabolite difference between HW and SW. Cellulose, hemicellulose, and lignin, which are the major components of wood, generally do not exhibit color; wood color is due to the presence of colored extractives contained in the wood. These colored extractives turn from pale to dark due to oxidation and polymerization over time.

Secondary Metabolites Identified in the Flavonoid Pathway in HW and SW
Eleven significantly different secondary metabolites in the HW and SW of red-heart Chinese fir were selected for further analyses of the flavonoid pathway, and the differences are shown in Figure 5. The contents of naringenin chalcone, dihydrokaempferol, pinocembrin, hesperetin, pinobanksin, eriodictyol, luteolin, catechin, and apigenin were significantly higher in the HW than in SW, while the contents of epicatchin and myricetin were significantly higher in the SW than in HW. The annotation of the differential secondary metabolites to the flavonoid pathway, as shown in Figure 6, revealed that the Log 2 FC values of naringenin chalcone, dihydrokaempferol, pinocembrin, and hesperetin were higher than 10 and were mainly concentrated in the upstream pathway. Myricetin, a downstream compound of dihydromyricetin, was more prevalent in SW than in HW, with a Log 2 FC value of 10.6. eriodictyol, luteolin, catechin, and apigenin were significantly higher in the HW than in SW, while the contents of epicatchin and myricetin were significantly higher in the SW than in HW. The annotation of the differential secondary metabolites to the flavonoid pathway, as shown in Figure 6, revealed that the Log2FC values of naringenin chalcone, dihydrokaempferol, pinocembrin, and hesperetin were higher than 10 and were mainly concentrated in the upstream pathway. Myricetin, a downstream compound of dihydromyricetin, was more prevalent in SW than in HW, with a Log2FC value of 10.6.

Discussion
We compared the secondary metabolites of the HW and SW in the red-heart Chinese fir by widely targeted metabolomics analyses, based on the UEMS system. There was a significant difference in the presence of 80 secondary metabolites. Metabolic pathway analyses revealed that the flavonoid Forests 2020, 11, 897 9 of 13 biosynthesis pathway was significantly different between HW and SW. Furthermore, HW contained significantly more flavonoids than SW.
There are considerably more metabolites in the plant kingdom than in the animal kingdom, with the number estimated to exceed 200,000 [31]. New varieties of woody plants are deliberately selected for the creation of high-quality wood. However, differences have only recently been discovered in the distribution of secondary metabolites in the stems of the red-heart Chinese fir. A metabolomics study can provide new information on the different secondary metabolic compound profiles. Detailed metabolite profiling of thousands of plant samples has great potential to elucidate metabolic processes. However, it is difficult to simultaneously undertake both comprehensive and high-throughput analyses in plants due to the wide diversity of metabolites present. Widely targeted metabolomics approach can monitor both the specific precursor ions and the product ions of each metabolite using MRM and tandem quadrupole mass spectrometry (TQMS), as they enable high sensitivity, reproducibility, and a broad dynamic range [32]. In general, such analyses can provide a large data set that will aid understanding of plant metabolism, and which can be used in combination with other omics approaches to achieve a deeper understanding of the biological processes involved. Herein, the UEMS system-based widely targeted metabolomics analyses approach appeared be effective as it helps us to rapidly obtain information on 80 significantly different metabolites between HW and SW and to classify them accurately in Chinese fir.
Color differences are widely studied in horticulture and agriculture, and particularly in plants that are usually propagated vegetatively, such as most fruit trees. Cho et al. [33] analyzed the metabolites of three colored potato cultivars and found differences in the anthocyanin content. Xue et al. [34] analyzed the molecular and metabolic bases of pigmentation in Lonicera japonica flowers at different developmental stages and constructed regulatory networks of anthocyanin biosynthesis, chlorophyll metabolism, and carotenoid biosynthesis by weighted gene co-expression network analysis. In addition, there have been color-related studies on fig (Ficus carica L.), loquat (Eriobotrya japonica Lindl), pomegranate (Punica granatum L.), and other fruit trees [35][36][37][38][39]. Anthocyanins and flavonoids affect the colors and tastes of fruits; their antioxidant capacities confer health properties and reduce the risk for cardiovascular morbidity and mortality [40,41]. As with many secondary metabolite pathways, the flavonoid pathway is regulated by multiple genes. First, phenylalanine ammonia-lyase (PAL) catalyzes the conversion of phenylalanine into cinnamic acid. Then, dihydrokaempferol is converted from cinnamic acid by a series of enzymes, such as cinnamate 4 hydroxylase (C4H), 4-coumaroyl: CoA-ligase (4CL), chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), flavanone 3 -hydroxylase (F3 H), and flavanone 3 -5 -hydroxylase (F3 5 H). Subsequently, dihydroflavonol 4-reductase (DFR) catalyzes the conversion of dihydrokaempferol into unmodified and colorless anthocyanins [42,43]. Then, the conversion of colorless anthocyanins into colored anthocyanins is catalyzed by anthocyanidin synthase (ANS). Finally, the unstable colored anthocyanins are converted into blue-purple, brick red, or magenta glycosides by UDP glucose: flavonoid 3-O glucosyltransferase (UFGT) [44][45][46]. In this study, we identified the flavonoids in the HW of the red-heart Chinese fir, determined which substances differed from those in the SW, and more importantly, revealed the highly significant accumulation of naringenin chalcone, dihydrokaempferol, pinocembrin, hesperetin, and other important secondary metabolites in the flavonoid biosynthesis pathway. We found that flavonoids were the secondary metabolites that differed most between the SW and HW in the red-heart Chinese fir. In addition, HW with a natural red-orange color differed from SW due to the presence of pigments that proved difficult to extract but which were formed by the enzymatic reduction of dihydroquercetin, the main extractive of Douglas fir HW [47,48]. Pâques et al. [49] found that taxifolin and dihydrokaempferol showed significant differences among different varieties of larch by studying the distribution of HW extractives in hybrid larches and in their related European and Japanese larch parents. Takahashi and Mori [50] concluded that Sugi HW turns black because sequirin-C, a type of norlignan that is readily soluble in alkaline solution and can form a large intramolecular conjugation system when alkalized, was converted into products that had a deep purple color as the HW was basified. In another study, the metabolic profiles of HW and SW in Taxus chinensis were analyzed using widely targeted metabolomics assay, and a total of 607 metabolites were detected, including 71 flavonoids and isoflavones that were significantly different between HW and SW [51]. The differences in the secondary metabolites of HW are mainly a result of species differences. This is due to the great diversity of metabolic pathways that each plant species has evolved to survive under varying environmental conditions. In our study, flavonoids were the main secondary metabolites that differed between the HW and SW in the red-heart Chinese fir, but it is not known if they underlie the differences in color, as this requires additional evidence. Anthocyanins, as downstream products in the flavonoid biosynthesis pathway, are widely distributed in flowers and fruits, where they play a role in coloring, but there are few reports of anthocyanins in wood. In the transition zone, the ray parenchyma cells synthesize flavonoids before programmed death and eventually become a part of the heartwood [52,53]. This process increases the wood quality and corrosion resistance, while giving the heartwood a more attractive color. In future studies, we will compare the secondary metabolites of HW between the red-heart Chinese fir and the common Chinese fir at a population scale, in the hope that it will address this issue more intuitively. In addition, metabolomics, in combination with genomics, transcriptomics, or proteomics, may help us to analyze molecules with similar chemical properties that are related to metabolites (namely, DNA, RNA, and proteins).

Conclusions
This study was the first to investigate the differential secondary metabolites of the red-heart Chinese fir using a widely targeted metabolomics method. We found significant differences between HW and SW. This study successfully identified components in the HW of the red-heart Chinese fir that are consistent with specific characteristics of HW in other conifers. The results provide critical metabolite inventory information for the study of specific metabolites in the HW of the red-heart Chinese fir, and provide a base study for the improvement of wood quality regarding to HW color on the metabolite aspect.