Nutritional Component Analyses in Different Varieties of Actinidia eriantha Kiwifruit by Transcriptomic and Metabolomic Approaches

Actinidia eriantha is a unique germplasm resource for kiwifruit breeding. Genetic diversity and nutrient content need to be evaluated prior to breeding. In this study, we looked at the metabolites of three elite A. eriantha varieties (MM-11, MM-13 and MM-16) selected from natural individuals by using a UPLC-MS/MS-based metabolomics approach and transcriptome, with a total of 417 metabolites identified. The biosynthesis and metabolism of phenolic acid, flavonoids, sugars, organic acid and AsA in A. eriantha fruit were further analyzed. The phenolic compounds accounted for 32.37% of the total metabolites, including 48 phenolic acids, 60 flavonoids, 7 tannins and 20 lignans and coumarins. Correlation analysis of metabolites and transcripts showed PAL (DTZ79_15g06470), 4CL (DTZ79_26g05660 and DTZ79_29g0271), CAD (DTZ79_06g11810), COMT (DTZ79_14g02670) and FLS (DTZ79_23g14660) correlated with polyphenols. There are twenty-three metabolites belonging to sugars, the majority being sucrose, glucose arabinose and melibiose. The starch biosynthesis-related genes (AeglgC, AeglgA and AeGEB1) were expressed at lower levels compared with metabolism-related genes (AeamyA and AeamyB) in three mature fruits of three varieties, indicating that starch was converted to soluble sugar during fruit maturation, and the expression level of SUS (DTZ79_23g00730) and TPS (DTZ79_18g05470) was correlated with trehalose 6-phosphate. The main organic acids in A. eriantha fruit are citric acid, quinic acid, succinic acid and D-xylonic acid. Correlation analysis of metabolites and transcripts showed ACO (DTZ79_17g07470) was highly correlated with citric acid, CS (DTZ79_17g00890) with oxaloacetic acid, and MDH1 (DTZ79_23g14440) with malic acid. Based on the gene expression, the metabolism of AsA acid was primarily through the L-galactose pathway, and the expression level of GMP (DTZ79_24g08440) and MDHAR (DTZ79_27g01630) highly correlated with L-Ascorbic acid. Our study provides additional evidence for the correlation between the genes and metabolites involved in phenolic acid, flavonoids, sugars, organic acid and AsA synthesis and will help to accelerate the kiwifruit molecular breeding approaches.


Introduction
Actinidia eriantha, commonly known as kiwifruit, belongs to the genus Actinidia. It is a unique wild germplasm resource in China and is widely distributed in the south [1]. The fruit of A. eriantha is easily peeled and has a high nutrient content, e.g., the content of ascorbic acid (AsA) is 1176 mg/100 g FW, which is three or four times that in A. chinensis (298 mg/100 g FW) [2,3], and the content of the total phenol (1200 mg/100 g FW) is more than ten times than that in A. chinensis (214 mg/100 g FW) [4]. The flower of A. eriantha is beautiful, with the petals in different shades of pink, while the root has antitumor pharmacological properties [5,6]. It has always been recognized as a novel berry with   (Table S1). Based on the cluster and PCA analysis, nine samples were clearly divided into three groups (Figure 2a,b). The value of PC1 and PC2 were 38.73% and 31.78%, respectively, and the two principal components accounted for 70.51% of the total variance. This suggested a difference in metabolic phenotypes among the three A. eriantha varieties. The large-scale analysis identified a total of 417 metabolites in MM-11, MM-13 and MM-16 samples (Table S1), which were categorized into more than 15 classes, mainly including flavonoids (14.38%), lipids (13.90%), amino acids and derivatives (13.18%), phenolic acids (11.51%), organic acids (7.19%), and terpenoids (6.71%) ( Figure 2c). Phenolics, including 48 phenolic acids, 60 flavonoids, 7 tannins and 20 lignans and coumarins, accounted for 32.37% of the total metabolites. The flavonoids were composed of 25 flavonols, 18 flavonoids, 7 dihydroflavones, 5 flavanols, 3 anthocyanins, and 1 chalcone and flavonoid carbonoxide. Though 48 metabolites were phenolic acids, the main phenolic acids in A. eriantha were identified as vanillic acid-glucoside, quillaic acid, coniferin, protocatechuic acid-4-glucoside, 2,5-dihydroxy benzoic acid o-hexside and 3,4,5-trimethoxyphenyl-β-D-glucopyranoside. Epigallocatechin gallate and six proanthocyanidins (A3, B1, B2, B3, B4 and C2) belong to tannins (Table S1). The organic acids include 30 metabolites, and the main acids are citric acid, quinic acid, succinic acid and D-xylonic acid. Twenty-three metabolites were detected as sugars, with the main metabolite being glucose, sucrose, arabinose and melibiose (Table S1). Of the metabolites, 411 were detected in MM-11, 415 in MM-13 and 412 metabolites in MM-16 ( Figure 2d). These results suggested that these three A. eriantha fruit varieties shared similar metabolite components but distinct metabolite expression patterns.

Dynamic Metabolic Changes of Different Varieties
In order to discover the changes in the pattern of metabolite expression in the three varieties, we analyzed their DAMs with the following parameters of VIP ≥ 1.0 and fold change ≥2 or ≤0.5. As a result, a total of 165 DAMs were identified between MM-11 and MM-13, including 81 up-regulated metabolites and 84 down-regulated metabolites in MM-13 ( Figure 3a and Table S2). A total of 140 DAMs were detected between MM-11 and MM-16, divided into 43 up-regulated metabolites and 97 down-regulated ( Figure 3b and Table S3). Among 121 DAMs detected between MM-13 and MM-16, 38 metabolites were up-regulated and 83 down-regulated in MM-16 (Figure 3c and Table S4). In all, 236 DAMs were identified in three comparable groups, which could be categorized into more than 15 classes. The flavonoids and phenolic acids were the majority of DAMs in the three groups (Table 1). With multiple comparative analysis, 28 DAMs were detected in the three cultivars (Figure 3d and Table S5), with almost half of them being flavonoids and phenolic acid. For the flavonoids, there were significant decreases in the content of cyanidin-3-O-rutinoside and eriodictyol C-hexoside in MM-13. The contents of narirutin, nobiletin, poncirin and isosinensetin were lower in MM-11 than MM-13 and MM-16, and chrysoeriol-O-acetylhexoside was only detected in MM-13 and MM-16. For phenolic acids, MM-11 had lower content of protocatechuic acid-4-glucoside, caffeic acid and oxalic acid compared with MM-13 and MM-16 (Table S5).

Transcriptome Analysis
The cDNA libraries of MM-11, MM-13 and MM-16 mature fruits were constructed with three biological replicates. The 388.02 million clean reads obtained (Table S6) were aligned to a reference genome and the expression levels of the genes were quantified. About 84% of reads were successfully mapped to the reference genome, and approximately 80% of reads were uniquely mapped. The total of 39,057 expressed genes identified in MM-11, MM-13 and MM-16 all shared similar distributions of the number of genes at different expression levels ( Figure 4a). Nearly 30% of these 39,057 expressed genes were not expressed in all three varieties, with the FPKM values lower than one. Genes expressed at a low level (10 > FPKM ≥ 1) accounted for the highest proportion, followed by those expressed at 100 > FPKM ≥ 10, with the highly expressed genes (FPKM ≥ 100) accounting for the lowest proportion. We compared the expressed genes in three varieties and found 85.

Expression of Phenolic Acid, Flavonoid and Aanthocyanidin Biosynthesis Genes in Three A. eriantha Varieties
We searched phenolic acid, flavonoid and anthocyanin biosynthesis pathways of kiwifruits based on the detected metabolites in reference to the phenylpropanoid biosynthesis pathway in the KEGG database ( Figure 5). A total of 80 genes were found to be involved in these pathways ( Figure S1a). Comparison of the 64 DEGs identified in the phenolic acid, flavonoid and anthocyanidin biosynthesis pathways revealed the difference of phenolics in the three varieties ( Figure S1b Table 3). MYB, bHLH and WD40 transcription factor families play roles in regulating the expression of the structural genes in phenylpropanoid, flavonoid and anthocyanins biosynthesis pathways [35,36]. The MYB and bHLH genes of A. eriantha were selected to construct a phylogenetic tree with Arabidopsis MYB TFs and function bHLH TFs in other species, respectively. We identified 12 MYBs involved in phenylpropanoid, flavonoid and anthocyanins biosynthesis pathways ( Figure S2). Phylogenetic analysis of functional MYBs from other species and 12 AeMYBs showed they can be divided into five clusters (Figure 6a), and their expression level in the three varieties is shown in Figure 6b. DTZ79_01g05950 had homology with AcMYB10 (Acc00493) and about 22-fold and 7.7-fold higher expression in MM-13 and MM-16 than in MM-11. Three bHLH TFs were clustered in the functional bHLH clade ( Figure S3). DTZ79_28g03140 (bHLH) encodes a protein that is highly homologous to the protein of AcbHLH5 (Acc19563), and DTZ79_03g08300 encodes a protein that is highly homologous to VvWDR1. We also identified one WD40 involved in phenylpropanoid, flavonoid and anthocyanins biosynthesis pathways (Figure 6b). The expression of four MYB genes (DTZ79_07g00600, DTZ79_12g00700, DTZ79_02g10790 and TZ79_18g00610) and WD40 (DTZ79_03g08300) were highly correlated with structural genes (Table 3).

Soluble Sugar, Organic Acid and AsA in Three A. eriantha Varieties
Soluble sugar and organic acid are the main determinants of fruit taste and also important regulatory signals of fruit ripening. During the fruit maturation stage, starch begins to hydrolyze and sugars accumulate in kiwifruit [13]. The sugar and organic acid metabolism pathways of A. eriantha fruits were constructed based on the detected metabolites in reference to the starch, sucrose and organic acid biosynthesis in the KEGG database (Figure 7). Thirteen metabolites involved in the starch, sucrose and organic acid biosynthesis were detected, including nine organic acids (citric acid, quinic acid, succinic acid, γ-aminobutyric acid, malic acid, trans-citridic acid, fumaric acid, shikimic acid and oxaloacetic acid) and four saccharides (glucose, sucrose, glucose-1-P and trehalose 6-P) (Figure 7). Four DAMs found by the two-by-two comparison of the three varieties were succinic acid, malic acid, trans-citridic acid and shikimic acid. The expression of starch biosynthesis-related genes, including AeglgC, AeglgA and AeGEB1, were at low or midlevels (FPKM < 100) in the three varieties ( Figure 7). However, the expression of AeamyA and AeamyB, which hydrolyze starch into maltose, was high. We identified seven AeamyB and two AeamyA genes, with AeamyB generally more highly expressed than AeamyA.
The expression of DTZ79_24g11560 (amyB) was consistent with the content of rehalose 6-phosphate (Table 3). The expression of sucrose synthase (SUS) and sucrose phosphate synthase (SPS), the key enzymes in sucrose biosynthesis, was high, with the FPKM value of more than 100. Both sucrose synthase genes were more highly expressed in MM-11 than in MM-13 and MM-16. Invertase (Inv) can hydrolyze sucrose into glucose and fructose, and hexokinase (HK) catalyzes fructose and glucose [37]. The expression of AeInv was higher in MM-11 and MM-16 compared to MM-13, with a mid-level of expression, while the expression of AeHK was lower than that of AeInv. These results suggested that starch was converted to soluble sugar during fruit maturation. The main organic acid in the A. eriantha fruit included quinic acid, citric acid, succinic acid, malic acid and γ-Aminobutyric acid. We scanned the expression of genes related to organic acid synthesis. AeCS, AeMDH1 and AeIDH1 were more highly expressed in all three, while expression of the quinic acid and shikimic acid-related genes aroB and aroD were at a middle level ( Figure 7).
As kiwifruit is one of the highest nutritional value fruits as it is rich in ascorbic acid (vitamin C, AsA), we investigated the genes involved in the four ascorbic acid biosynthesis pathways and recycling pathways in kiwifruit: the L-galactose pathway, D-galacturonic acid pathway, inositol pathway and L-gulose pathway (Figure 8) [38]. The AeGGP (GDP-L-galactose phosphorylase) gene had a high expression level, as did the AeAO, AeAPX, AeMDHAR and DHAR genes involved in the ascorbic acid recycling pathway.

Validation of Gene Expression Level in Three Varieties
To confirm the gene expression pattern of the A. eriantha fruit identified by RNA-Seq data, ten DEGs were randomly selected for validation using qRT-PCR, with the primers shown in Table S7. The results of the expression pattern of qPCR were consistent with the RNAseq data ( Figure S4), which confirmed that the gene expression profile of RNA-seq data was reliable.

Discussion
Metabolome and transcriptome analyses were widely used for studies of sugar, flavor, flavonoids and anthocyanin formation and early maturation traits in kiwifruit [13,[39][40][41]. Here, we firstly investigated the nutritional components of A. eriantha based on metabolome and transcriptome sequencing, then comprehensively analyzed the main nutrient component of A. eriantha fruit, including phenolic acid, flavonoids, sugars, organic acid and AsA acid. A total of 417 metabolites were identified, and 135 of them were found to be phenolic compounds. These compounds are strong antioxidants that have been found in kiwifruit, with the content higher in A. eriantha than in A. deliciosa and A. chinensis [4,22]. Phenolic acids and flavonoids are the major secondary metabolites in fruits [14,15,42,43], and the biosynthesis of phenolic compounds in plants is primarily derived via the phenylpropanoid pathway [44]. PAL, C4H and 4CL are major enzymes involved in the biosynthesis of many secondary metabolites, including phenolic acid, flavonoids and lignin. F5H, COMT, CAD, CCR and UGT72E enzymes mediate the synthesis of different phenolic acid derivatives. CHS, CHI, F3H, FLS, DFR and ANS enzymes are involved in flavonoids [45]. LAR and ANR enzymes lead to catechin and epicatechin, respectively [46,47]. In our study, PAL (DTZ79_15g06470), 4CL (DTZ79_26g05660 and DTZ79_29g0271), CAD (DTZ79_06g11810), COMT (DTZ79_14g02670) and FLS (DTZ79_23g14660) were highly correlated with the corresponding metabolites, suggesting they may play roles in regulating the synthesis of phenolic compounds in A. eriantha. Previous studies have shown that transcription factors, including MYB, bHLH and WD40, regulate the phenylpropanoid, flavonoid and anthocyanin biosynthesis pathways in different plants [44,48]. The homologs of DTZ79_28g03140 and DTZ79_01g05950 in A. chinensis were AcbHLH5 (Acc19563) and AcMYB10 (Acc00493), respectively. AcbHLH5 has been reported to be the partner protein of AcMYB10 and AcMYB110 and plays roles in the regulation of anthocyanin accumulation [41,[49][50][51]. AchMYC2, the homolog of DTZ79_07g03850, can activate PAL, C4H and 4CL expression in kiwifruit [52]. DTZ79_18g00610 is close to VvMYBPA1, which has been reported to regulate proanthocyanidin synthesis during fruit development in grape [53,54]. DTZ79_28g03140, DTZ79_01g05950, DTZ79_07g03850 and DTZ79_18g00610 may also play roles in the regulating structural genes in flavonoid biosynthesis in A. eriantha.
Based on the metabolic characteristics of kiwifruit, starch is accumulated in the development stage, while in the maturation and ripening stages, starch is degraded and glucose, fructose and sucrose are accumulated [26,55]. In our study, the expression of the starch synthesis-related genes glgC, glgA, GBE1 was low compared with the starch degradation-related genes amyA and amyB, and the sucrose synthesis-related genes SPS and SUS were highly expressed ( Figure 5). These results are consistent with the conversion of starch into sugar during the maturation and ripening stage of kiwifruit. The homolog of the amyB genes DTZ79_14g07050 and DTZ79_14g07230 in A. deliciosa is AdBAM3L, which is a key structural gene in starch degradation [26,31]. These two genes may also have roles in starch degradation, and this needs to be further investigated. The organic acid types and component contents of kiwifruit vary among genotypes [56]. In the ripening stage, the main organic acid accumulated in MM-11, MM-13 and MM-16 fruits included quinic acid, citric acid and malic acid, as with other Actinidia species [32]. The MDH1 (malate dehydrogenase) and IDH1 (isocitrate dehydrogenase) had a high expression level, which may be consistent with the high content of citric acid and malic acid in mature fruit. The expression of DTZ79_23g14440 (MDH1) was highly correlated with malic acid (r = 0.99), which may be the key structural gene in regulation of malic acid biosynthesis. Kiwifruit have an extremely high content of ascorbic acid, which varies among genotypes [57]. Generally, the content of AsA acid is higher in A. eriantha fruit than in A. chinensis or A. deliciosa [58]. In the present study, we investigated genes in the AsA biosynthesis and recycling pathways (Figure 8). The AeGGP (GDP-L-galactose phosphorylase) genes were expressed two to eight times more than the other genes in the AsA biosynthesis pathway, indicating the L-galactose pathway is the major route of AsA synthesis in kiwifruit [58]. The expression of the genes involved in the L-galactose pathway was higher than that in the other three pathways. We also detected genes involved in the AsA recycling pathway, including MDHAR (monohydroascorbate reductase) and DHAR (droascorbate reductase), which are responsible for ascorbic acid regeneration from its oxidized forms, and APX (L-ascorbate peroxidase) and AO (L-ascorbate oxidase) that oxidize AsA to monohydroascorbate and dehydroascorbate, respectively. Our study provides a comprehensive insight into the main nutrient component of A. eriantha fruit. Based on the genes highly correlated to phenolic acid, sugar, organic acid and ascorbic acid, specific molecular markers will be developed and applied to efficient and targeted kiwifruit selection, evaluation and improvement.

Fruit Materials
Three varieties of A. eriantha, 'MM-11', 'MM-13' and 'MM-16', were used for transcriptome and metabolomics sequencing. All three varieties were more than 3 years old after grafting on 8-year-old 'Jinkui' (A. deliciosa) rootstock in a Shanwei kiwifruit germplasm resource nursery located in Fengxin County (28 • N, 114 • E, elevation 76 m) Jiangxi province, PR China. Mature fruit of the three varieties were collected with three biological replicates and immediately frozen in liquid nitrogen and stored at −80 • C for RNA-Seq and metabolic assays.

Metabolome Data Analysis
Three replicates of ripe fruit of MM-11, MM-13 and MM-16 were used for metabolite profiling with technology of the Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China) (http://www.metware.cn/) (accessed on 5 August 2022), using a widely targeted metabolome method. The freeze-dried fruit was crushed into powder using a mixer mill (MM400, Retsch) and 100 mg of powder was extracted overnight at 4 • C with 0.6 mL 70% aqueous methanol. Following centrifugation at 10,000× g for 10 min, the extracts were filtered and then analyzed on an UPLC-MS/MS system with the following analytical conditions: UPLC: column, Agilent SB-C18. The mobile phase consisted of pure water (Solvent A) and acetonitrile (Solvent B), both acidified with 0.1% (v/v) formic acid. The mobile phase gradient was run from 5% Solvent B at 0 min to 95% Solvent B at 9.0 min and maintained for 1 min, then the 5% Solvent B was adjusted at 10-11.1 min and maintained for 3.9 min. The flow velocity was set as 0.35 mL/min. The column oven temperature was adjusted to 40 • C, and the injection volume was 4 µL. The effluent was connected to an ESI-triple quadrupole-linear ion trap (Q TRAP)-MS, then operated in positive and negative ion mode and controlled by Analyst 1.6.3 software (AB Sciex, Concord, ON, Canada). The ESI source operation following parameters: ion source temperature was 550 • C, ion spray voltage (IS) 5500 V (positive ion mode)/−4500 V (negative ion mode). Ion source gas I (GSI), gas II (GSII) and curtain gas (CUR) were set at 50, 60 and 25 psi, respectively. The collision-activated dissociation (CAD) was high. Instrument tuning and mass calibration was performed with 10 and 100 µmol/L polypropylene glycol solution in QQQ and LIT modes, respectively. QQQ scans were using MRM models with collision gas (nitrogen) set to medium. Quantification of metabolites was carried out using an MRM method. The differentially accumulated metabolites (DAMs) were identified with the following criteria: variable importance in projection (VIP) ≥ 1 and a fold change ≥2 or ≤0.5.

Transcriptome Sequencing and Analysis
A total of nine samples (three replicates each of MM-11, MM-13 and MM-16) were prepared for RNA extraction based on the instructions of the Quick RNA isolation Kit (Waryong, Beijing). The quality of total RNA was analyzed using the Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). Sequencing libraries were prepared with a NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) following the manufacturer's instructions. Transcriptome sequencing was performed on an Illumina HiSeq 2500 platform with PE125 from the Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China). After removing the adapter sequences, ambiguous nucleotides, and lowquality sequences, the clean reads from each library were mapped to the A. eriantha (White) genome (http://kiwifruitgenome.org/) (accessed on 5 August 2022) using HISAT2 [59] to calculate the mapping ratio. The level of gene expression was measured using FPKM (fragments per kilobase per million reads) [60]. Differentially expressed genes (DEGs) between two groups were identified using the DESeq2 R package, with a minimal two-fold difference in expression (|log2 Ratio| ≥ 1) and p-value < 0.05. Gene annotation was performed using the NCBI non-redundant protein sequences (Nr) (https://ftp-private.ncbi.nlm.nih.gov), the protein family (Pfam) (https://pfam.xfam.org/) (accessed on 5 August 2022), the Swiss-Prot protein database (http://www.expasy.ch/sprot) (accessed on 5 August 2022)and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.genome.jp/kegg/) (accessed on 5 August 2022). Gene ontology (GO) annotation was used for sequences with a match in the Nr database by using Blast2GO v.3.0 [61]. The GO enrichment and KEGG enrichment analysis of DEGs were conducted using TBtools, with FDR < 0.05 regarded as significantly enriched in DEGs [62]. Heatmaps were prepared using TBtools [62]. Correlation analysis of transcriptome and metabolite data were performed by using excel, with a Pearson correlation coefficient (PCC) > 0.9 and p-value <0.05. The data were log2-transformed before correlation analysis.

Quantitative Real-Time PCR (qRT-PCR) Validation
The total RNA of MM-11, MM-13 and MM-16 were extracted using the CTAB method. The quality and concentration of RNA were checked on a gel and measured using a BioDrop spectrophotometer (Biochrom, Cambridge, UK). The qRT-PCR primers were designed using Primer3 software (http://primer3.ut.ee/) (accessed on 5 August 2022). The qRT-PCR reaction was performed on an ABI PRISM 7900HT sequence detection system (Applied Biosystems, Waltham, MA, USA) using the SYBR Premix Ex Taq Kit (TaKaRa, Tokyo, Japan) with the following reaction conditions: 95 • C for 3 min, 40 cycles of 95 • C for 15 s, 56 • C for 30 s, and 72 • C for 20 s. Actin was used for normalization, and the expression data were calculated by the 2 ∆∆Ct formula [63].

Conclusions
China has abundant germplasm resources of A. eriantha, which can be explored for modern kiwifruit breeding. Metabolomics sequencing is a good method for evaluating the nutrient profile of elite accession of A. eriantha prior to breeding. In the present study, we investigated the metabolites of three A. eriantha elite lines by using a UPLC-MS/MSbased metabolomics approach and transcriptome. We identified a total of 417 metabolites, including 135 polyphenols, 32 sugar-related metabolites, 30 organic acids. Further analysis of the biosynthesis and metabolism of phenolic acid, flavonoids, sugars, organic acid and ascorbic acid in A. eriantha fruit indicated that starch was converted to soluble sugar during fruit maturation, and the L-galactose pathway is the primary metabolism of ascorbic acid. Furthermore, the expression level of PAL, 4CL, CAD, COMT and FLS genes were highly correlated with polyphenols content; ACO, CS, fumA, MDH1, and GPI were correlated with organic acid. SUS and TPS were responsible for trehalose 6-phosphate, and GMP and MDHAR were related to ascorbic acid. Our study provides additional evidence for the correlation between genes and traits. Molecular markers associated with traits can be further developed and used in molecular breeding approaches for kiwifruit.

Conflicts of Interest:
The authors declare no conflict of interest.