Complementary transcriptomic and proteomic analyses of a chlorophyll-deficient tea plant cultivar reveal multiple metabolic pathway changes

    Complementary transcriptomic and proteomic analyses of a chlorophylldeficient tea plant cultivar reveal multiple metabolic pathway changes Lu Wang, Hongli Cao, Changsong Chen, Chuan Yue, Xinyuan Hao, Yajun Yang, Xinchao Wang PII: DOI: Reference:

S1874-3919(15)30110-X doi: 10.1016/j.jprot.2015.08.019 JPROT 2261

To appear in:

Journal of Proteomics

Received date: Revised date: Accepted date:

14 May 2015 4 August 2015 27 August 2015

Please cite this article as: Wang Lu, Cao Hongli, Chen Changsong, Yue Chuan, Hao Xinyuan, Yang Yajun, Wang Xinchao, Complementary transcriptomic and proteomic analyses of a chlorophyll-deficient tea plant cultivar reveal multiple metabolic pathway changes, Journal of Proteomics (2015), doi: 10.1016/j.jprot.2015.08.019

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ACCEPTED MANUSCRIPT Title:

Complementary

transcriptomic

and

proteomic

analyses

of

a

chlorophyll-deficient tea plant cultivar reveal multiple metabolic pathway changes

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Lu Wang1,2*, Hongli Cao1,2*,Changsong Chen3*, Chuan Yue1,2, Xinyuan Hao1,2,

National Center for Tea Plant Improvement, Tea Research Institute, Chinese

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1

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Yajun Yang1,2, Xinchao Wang1,2

Academy of Agricultural Sciences, Hangzhou 310008, China Key Laboratory of Tea Biology and Resources Utilization, Ministry of

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2

Agriculture, Hangzhou 310008, China 3

Tea Research Institute, Fujian Academy of Agricultural Sciences, Fu’an

355000, China

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E-mail addresses:

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*These authors contributed equally to this work

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Lu Wang: [email protected]; Cao Hongli: [email protected]; Changsong Chen: [email protected]; Yue Chuan: [email protected];

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Xinyuan Hao: [email protected]

Corresponding authors: Yajun Yang, Tel: +86-571-86650226, E-mail: [email protected] Xinchao Wang, Tel: +86-571-86653162, E-mail: [email protected]

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ACCEPTED MANUSCRIPT Abstract To uncover the mechanisms that underlie the chlorina phenotype of the tea plant, this study employs morphological, biochemical, transcriptomic, and iTRAQ-based

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proteomic analyses to compare the green tea cultivar LJ43 and the yellow-leaf tea

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cultivar ZH1. ZH1 exhibited the chlorina phenotype, with significantly decreased chlorophyll content and abnormal chloroplast development compared with LJ43. ZH1 also displayed higher theanine and free amino acid content and lower carotenoid and

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catechin content. Microarray and iTRAQ analyses indicated that the differentially expressed genes and proteins could be mapped to the following pathways:

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‘phenylpropanoid biosynthesis,’ ‘glutathione metabolism,’ ‘phenylanine metabolism,’ ‘photosynthesis,’ and ‘flavonoid biosynthesis.’ Altered gene and protein levels in these pathways may account for the increased amino acid content and reduced chlorophyll and flavonoid content of ZH1. Altogether, this study combines transcriptomic and proteomic approaches to better understand the mechanisms

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responsible for the chlorina phenotype.

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(Camellia sinensis)

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Keywords: chlorina, chlorophyll deficiency, proteome, iTRAQ, microarray, tea plant

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ACCEPTED MANUSCRIPT Biological significance Chlorina tea plant breeding has recently become a topic of great interest. Chlorina plant leaves exhibit altered levels of chloropyll, carotenoid, catechin, and amino acids,

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all of which are important determinants of the quality of tea and its health effects.

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However, partially due to insufficient proteomic data, there is very limited understanding of the underlying mechanism for this altered phenotype. Here, we present a combined physiological, transcriptomic, and proteomic analysis that reveals

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several important pathways associated with the chlorina tea plant phenotype. This integrative analysis provides a better understanding of the molecular mechanisms that

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of new, high-quality tea cultivars.

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are responsible for the chlorina phenotype and may have applications in the breeding

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ACCEPTED MANUSCRIPT 1. Introduction The tea plant (Camellia sinensis (L.) O. Kuntze) is woody crop of worldwide economic importance [1]. Recently, there has been increased interest in tea plant

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germplasms, including leaf color cultivars [2, 3]. The chemical composition of tea

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leaves is an important determinant of the sensory quality and health effects of tea [4-6]. The compounds that determine tea flavour, astringency, and taste include catechins and free amino acids. Compared to normal green cultivars, albino and

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chlorina tea plant cultivars exhibit lower amounts of chlorophyll (Chl), carotenoid, and catechins; higher levels of amino acids (including theanine); and disrupted

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chloroplasts [7-10].

Mutants deficient in Chl synthesis or photosynthesis have been identified in many plants, inculding Arabidopsis [11, 12], rice [13, 14], barley [15], and maize [16], and they have been studied to elucidate the mechanisms of Chl metabolism, chloroplast development, and photosynthesis. Albino and chlorina plants are valuable genetic

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specimens for exploring the molecular mechanisms that regulate the off-white or yellowish color phenotype and chloroplast development. In recent years, a number of

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studies on albino or chlorina tea plant cultivars have been conducted [7, 10, 17-19]; however, the molecular mechanisms that produce the different phenotypes of these tea

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cultivars remain unclear. The changes in chemical composition and gene expression during periodic albinism in ‘Anji Baicha,’ a typical albino tea plant cultivar grown in

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China, have been extensively studied [7, 18-20]. The molecular mechanism underlying the chlorina phenotype has been most recently studied in the yellow-leaf tea cultivar named Zhonghuang 2 (ZH2) [8]. Differentially expressed genes between ZH2 and the normal green tea cultivar Longjing 43 (LJ43) were identified by comparing the transcriptomes of LJ43 and ZH2 using a microarray technique. A number of these differentially expressed genes were mapped to pathways related to amino acid metabolism, Chl biosynthesis, and flavonoid biosynthesis, which were considered to be responsible for the phenotype of ZH2 [8]. Transcriptomic analysis facilitates a deeper understanding of gene expression networks, but proteomics links these networks to protein products and provides further insight into posttranscriptional modifications [21]. Gene transcription data are insufficient for prediction of protein expression levels. Thus, knowledge of the tea plant genome provides little information about the proteins that underlie metabolic 4

ACCEPTED MANUSCRIPT and regulatory events. Large-scale proteomic data for the chlorina tea plant are still generally lacking. Using two-dimensional electrophoresis (2-DE) and mass spectrometry, proteomic analysis of young leaves in ‘Anji Baicha’ showed that

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proteins involved in metabolism of carbon, nitrogen, and sulfur; photosynthesis;

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protein processing; stress defense; and RNA processing play crucial roles in periodic albinism [17]. However, the 2-DE technique is limited to the analysis of medium- or high-abundance proteins [22, 23]. Isobaric tags for relative and absolute quantitation

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(iTRAQ) is currently one of the most robust mass spectrometry techniques, enabling quantitative comparison of protein abundance by measuring peak intensities of

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reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS [24]. Although this technique has been widely used to study many plants, it has not yet been used for proteomic profiling of tea plants.

Integrated analysis of large-scale transcriptomic and proteomic data can provide further insights into the mechanisms underlying different phenotypes of tea plants. In

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this study, we report our findings on the mechanism governing the chlorina phenotype through phenotypic, biochemical, microarray, and iTRAQ-based proteomic analyses.

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This study is the most extensive analysis of the transcriptome and proteome of a chlorina tea plant and reveals important pathways that underlie chlorophyll deficiency,

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thus improving our mechanistic understanding of the chlorina phenotype. 2. Materials and methods

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2.1 Plant material and sample preparation Two tea plant (Camellia sinensis (L.) O. Kuntze) cultivars, ‘LJ43’ and ‘ZH1,’ were used in this study. The plants were five years old and had been grown in the field at the Tea Research Institute of the Chinese Academy of Agricultural Sciences (TRI, CAAS, N 30°10', E 120°5'), Hangzhou, China. LJ43 is a normal green tea cultivar that is widely planted in China, especially in the Zhejiang province. ZH1 is a yellow-leaf cultivar that was selected from a natural yellow-leaf mutant in the Zhejiang province via systematic selection. The two cultivars do not share the same genetic background. The first new shoots (two leaves and one bud) of the cultivars were sampled in the spring. Three independent biological replicates were acquired. Each replicate was collected from more than ten randomly selected tea plants. The samples were divided 5

ACCEPTED MANUSCRIPT into two duplicate groups: one group was steamed for 5 min, dried at 80°C, and ground into powder for chemical analysis; and the other group was stored at -80°C after being flash-frozen with liquid nitrogen for microarray and iTRAQ analyses.

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2.2 Measurement of chemical composition

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Chl was extracted with 80% acetone from 100 mg of dried sample. The extract was measured spectrophotometrically at 645 nm and 663 nm. Chl content was determined

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according to the method of Arnon [25], while lutein and beta-carotene content were measured via HPLC as described previously [26]. Theanine and catechins were

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extracted from 100 mg of dried sample by heating at 90°C for 30 min in 50 mL of water, with shaking once every 10 min. The filtrates were diluted, refiltered through a 0.45 μm nylon filter, and analyzed by HPLC [27]. For the measurement of total free amino acids, the ninhydrin colorimetric method was used as described previously [8]. 2.3 Transmission electron microscopic (TEM) analysis

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The ultrastructure of the chloroplasts was investigated via TEM according to the method described by Du et al. (2008) and Wang et al. (2014) [8, 10].

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2.4 Microarray data analysis

For microarray and quantitative real-time PCR (qRT-PCR) analyses, total RNA was

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extracted from 0.5 g to 1 g of leaf samples from ZH1 and LJ43 tea plants as described previously [28]. The 60-mer oligonucleotide probes and microarray were designed by

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eArray (Agilent) [29]. The microarray data were deposited into the NCBI Gene Expression Omnibus (GEO) database under accession number GSE52255. Raw microarray data was acquired and analyzed as previously described [8]. A differentially expressed gene was defined as a variation in the gene expression test with a P-value < 0.05 and a fold change (FC) > 2. To identify biological pathways, genes were annotated with their corresponding enzyme commission (EC) numbers from BLASTX alignments against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [30, 31]. Fisher’s exact test was employed to identify significantly enriched pathways, and the criteria for significance were defined as a P-value < 0.05 and a false discovery rate (FDR) < 0.05 [32]. 2.5 Protein extraction, digestion, iTRAQ labeling, and mass spectrometry Total proteins were extracted from the leaf tissue of LJ43 and ZH1 plants as 6

ACCEPTED MANUSCRIPT previously described [21]. Protein content was determined by the Bradford method [33]. An equal amount of proteins was prepared, and protein samples were reduced with 10 mM DTT alkylated with 55 mM iodoacetamide, and were then digested using

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sequencing-grade trypsin (Promega, Madison, WI, USA) at a ratio of 1:20 (w:w) for

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12 h at 37℃. One hundred micrograms of digested protein were labeled using the iTRAQ 8-plex kit (Applied Biosystems, Framingham, MA, USA) according to the manufacturer’s protocol. After labeling, the samples were combined and lyophilized.

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The peptide mixture was dissolved in 4 mL of strong cation exchange (SCX) buffer A (25 mM NaH2PO4 in 25% acetonitrile, pH 2.7). The peptides were fractionated on an

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Ultremex SCX column (4.6×250 mm) using a Shimadzu LC-20AB HPLC system. Peptides were eluted at a flow rate of 1 mL/min with elution buffer B (25 mM NaH2PO4, 1M KCl in 25% acetonitrile, pH 2.7). The absorbance at 214 nm was monitored, and 20 fractions were collected. Each fractionated sample was dried and desalted, and then analyzed by liquid chromatography coupled with tandem mass

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spectrometry (LC-MS/MS) as described previously [34]. 2.6 Proteomic data analysis

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Protein identification was performed using Mascot 2.3.02 software with the tea plant UniGene translation database (771978 sequences). Sequences were obtained from

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NCBI and from transcriptome sequences from our own lab (Accession numbers SRX365198 and SRX202319). Search parameters were chosen as reported by Chu et [34].

A 1.2-fold

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al

cutoff

was

used

to

determine

up-accumulated

or

down-accumulated proteins, with a P-value < 0.05 [34, 35]. Functional annotation of proteins was conducted using Gene Ontology (GO) annotation [36]. The differentially accumulated proteins were assigned to the Cluster of Orthologous Groups of proteins (COG) database and the KEGG database [37]. 2.7 QRT-PCR Total RNA in all samples was extracted using a RNAprep Pure kit (Tiangen, Beijing, China) according to the manufacturer's instructions. Five micrograms of RNA were treated with RNase-free DNase I (Invitrogen, Carlsbad, CA, USA) to remove residual genomic DNA. First-strand cDNA was synthesized using SuperScript® III Reverse Transcriptase (Invitrogen). QRT-PCR was performed as described previously [8]. Triplicate quantitative assays were performed on each cDNA sample, and the 18S 7

ACCEPTED MANUSCRIPT rRNA reference gene was used as an internal control. Transcript levels were calculated relative to the level of 18S rRNA using the 2-ΔΔCt formula [38]. All data are presented as the mean ± SD (n=3). Primer sequences used for qRT-PCR are provided

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in Supplementary data 1.

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2.8 Statistical analysis of the data

Data are expressed as the mean ± SD from three independent biological replicates.

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Significance was determined via one-way analysis of variance (ANOVA), and for differences between groups, the least significant difference (LSD) t-test was

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employed (P < 0.05). 3. Results

3.1 Characterization of chlorophyll-deficient chlorina cultivar ZH1 When growing in the field, ZH1 plants exhibit yellow leaves, while LJ43 exhibits green leaves (Fig. 1A and 1B). The levels of Chl a and Chl b in new shoots (two

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leaves and one bud) of ZH1 were significantly lower than those in the shoots of LJ43 (approximately 20.5% and 8.3% of the Chl a and Chl b content of LJ43, respectively;

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Fig. 1E). As green leaf color is the result of Chl biosynthesis, a reduced Chl content is responsible for the chlorina leaf phenotype of ZH1.

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In addition to Chl, carotenoids are critical for the biogenesis of the photosynthetic apparatus. The major carotenoids found in tea leaves are beta-carotene and lutein [39].

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Our results showed that the lutein and beta-carotene contents of ZH1 were 63% and 54.7%, respectively, of the corresponding amounts in LJ43 (Fig. 1E). We also investigated the ultrastructure of the chloroplasts in ZH1, as the chloroplasts are responsible for leaf greening. Chl and other pigments that are involved in photosystems are located in the grana, which are made up of thylakoids. TEM analysis showed that LJ43 plants exhibited typical chloroplast ultrastructure, consisting of grana and thylakoids (Fig. 1C). In ZH1 chloroplasts, the thylakoids were present, but the stacks of grana were not observed (Fig. 1D). This result indicated that abnormal chloroplast development was responsible for the change in leaf color in the ZH1 cultivar. 3.2 Changes in the abundance of theanine, free amino acids, and catechins 8

ACCEPTED MANUSCRIPT Theanine is the most abundant free amino acid in tea plants and has been shown to be beneficial to human health [40-42]. New shoots of ZH1 and LJ43 were compared with respect to the abundance of theanine and free amino acids. ZH1 leaves had

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significantly higher amounts of theanine and total free amino acids compared with

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LJ43 leaves (Fig. 2). Catechins represent 60% to 80% of the total flavonoids in green tea [43]. The total catechin content of ZH1 shoots was 54.1% that of LJ43 shoots (Fig. 2).

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3.3 Microarray analysis of gene expression

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To elucidate the mechanisms underlying the phenotype of ZH1, gene expression in ZH1 and LJ43 plants was compared using a Camellia sinensis custom microarray [29]. We identified 5,944 differentially expressed genes (P-value < 0.5, FC > 2), including 2,751 up-regulated genes and 3,193 down-regulated genes (Fig. 3A). In agreement with what is known about the ZH1 phenotype, genes related to amino acid metabolism; photosynthesis; porphyrin and Chl metabolism; flavonoid biosynthesis; carotenoid

biosynthesis

were

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and

identified

(106

up-regulated

and

169

down-regulated) (Fig. 3A, Supplementary data 2, indicated in the blue circle). Among

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these genes, transcripts with significant differential expression were defined as those with P-value < 0.05 and fold change > 5 (Table 1). By annotating the 5,944

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differentially expressed genes with Gene IDs in KEGG, 261 pathways were mapped (Supplementary data 3). Twenty-three pathways were considered statistically

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significant with P-value < 0.05 and FDR < 0.05, including ‘porphyrin and chlorophyll metabolism,’ ‘flavonoid biosynthesis,’ ‘photosynthesis,’ ‘flavone and flavonol biosynthesis’, and ‘phenylalanine metabolism’ (Fig. 3B, Supplementary data 3). These pathways are suggested to be important to the phenotype of ZH1. 3.4 Proteomic analysis of chlorina tea cultivar We employed 8-plex iTRAQ experiments to characterize the proteomic profiles of LJ43 and ZH1 leaves. From the pooled data for ZH1 and LJ43, 3,131 unique proteins were identified, of which 437 were classified as differentially accumulated proteins (P-value < 0.05, FC > 1.2). Differentially acccumulated proteins were annotated and classified according to the ‘biological process (BP),’ ‘molecular function (MF),’ and ‘cellular component (CC)’ categories and their sub-categories. The two largest categories for each functional group were as follows: ‘metabolic process’ and ‘cellular 9

ACCEPTED MANUSCRIPT process’ for BP; ‘binding’ and ‘catalytic activity’ for MF; and ‘cell’ and ‘organelle’ for CC (Fig. 4A). Two hundred and fifty-seven differentially accumulated proteins were assigned to 21 categories using the COG database. The main functional categories

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were ‘posttranslational modification, protein turnover, chaperones’ (17.9%), ‘general

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function prediction only’ (15.6%), ‘translation, ribosomal structure and biogenesis’ (13.2%), ‘carbohydrate transport and metabolism’ (9.7%), ‘energy production and conversion’ (8.6%) and ‘amino acid transport and metabolism’ (6.2%) (Fig. 4B). The

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differentially accumulated proteins were further investigated using the KEGG database. Two hundred and eighty-seven differentially accumulated proteins were

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mapped to KEGG pathways (Supplementary data 4). Among these pathways, 25 pathways were enriched with more than 5 differentially-accumulated proteins; these pathways include ‘metabolic pathways’ (29.27%), ‘biosynthesis of secondary metabolites’ (19.51%), ‘photosynthesis’ (3.14%) and ‘flavonoid biosynthesis’ (2.44%) (Fig. 4C).

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3.5 Integrated analysis of transcriptomic and proteomic datasets Combining transcriptomic and proteomic datasets, the differentially expressed genes

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and proteins can be mapped to the following categories: ‘phenylpropanoid biosynthesis,’ ‘glutathione metabolism,’ ‘phenylanine metabolism,’ ‘photosynthesis,’

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and ‘flavonoid biosynthesis’ (Fig. 3B and 4C). Ten genes in the Chl biosynthesis pathway were differentially expressed (P-value <

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0.05, FC > 2; Fig. 5A - blue numbers). Of the ten genes, four were up-regulated and six were down-regulated in ZH1 compared with LJ43. On the posttranscriptional level, four proteins were differentially accumulated (P-value < 0.05, FC > 1.2; Fig. 5A - red numbers). Of these four proteins, one was up-accumulated and three were down-accumulated in ZH1 compared with LJ43. Two genes, EC1.3.1.33 (protochlorophyllide

reductase,

PCR)

and

EC3.1.1.14,

were

differentially

down-regulated on both the transcriptional and posttranscriptional levels in ZH1 (Fig. 5A). Several genes and proteins involved in photosynthesis were differentially regulated in ZH1 (Table 2). Thirty-four genes were differentially expressed (P-value < 0.05, FC > 1.5), including seven genes in PSI, eleven genes in PSII, two genes in the cytochrome b6/f complex, four genes in the photosynthetic electron transport, and ten genes in 10

ACCEPTED MANUSCRIPT LHC (Table 2). Among these genes, eight genes (PsbB, PsaB, PsaE, PsaF, PetA, PetF, Lhca3, and Lhcb4) were also regulated at the posttranscriptional level according to the iTRAQ proteomic data (Table 2). Notably, most of the differentially accumulated

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proteins were decreased in ZH1. These genes or proteins may therefore be associated

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with Chl deficiency.

Several genes and proteins involved in flavonoid biosynthesis were significantly regulated in ZH1 (Fig. 5B), which is consistent with the changes in catechin content

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observed in ZH1. Of eleven differentially expressed genes in the flavonoid biosynthesis pathway, three were up-regulated and eight were down-regulated in ZH1

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compared with LJ43. Four proteins were differentially down-accumulated in ZH1 compared with LJ43. Three genes (EC1.14.11.23 (flavonol synthase, FLS), EC1.3.1.77 (anthocyanidin reductase, ANR), and EC2.3.1.74 (chalcone synthase, CHS))

were

differentially

posttranscriptional levels.

down-regulated

on

both

transcriptional

and

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3.6 Validation of differentially expressed genes and proteins

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QRT-PCR was performed for 20 genes from the microarray analysis to validate their differential expression in ZH1 and LJ43 plants. For 16 out of 20 genes, the expression

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patterns determined by qRT-PCR corresponded well with those determined by microarray analysis [29]. QRT-PCR validation was performed for another 14 genes that were found to be differentially regulated on both the transcriptional and

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posttranscriptional levels. The expression patterns of 10 genes (PsbB, PsaB, PsaE, PsaF, PetA, Lhca4, ANR, FLS, CHS, and PCR) were similar to the microarray and proteomic data (Fig. 5, Fig. 6 and Table 2). The other four genes (PetF, Lhca3, EC3.1.1.14 and hemL) exhibited different expression patterns under qRT-PCR analysis compared with microarray and proteomic evaluation. This discrepancy may be attributed to several factors, including the posttranscriptional regulatory process and the different sensitivities of the analytical methods. 4. Discussion In this study, biochemical characteristics of two tea cultivars were compared, and their transcriptomes and proteomes were characterized using custom oligonucleotide-based microarrays [29] and iTRAQ, respectively. Differentially expressed genes and proteins between the two cultivars were identified. 11

ACCEPTED MANUSCRIPT Leaves are the economic and utilization parts of the tea plant, and they are also the primary sites of photosynthesis. Studies of the tea leaf proteome play an important role in understanding how proteins in certain pathways contribute to the chemical

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composition of the tea leaf, which affects the sensory and quality of the tea. In one

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study, proteomic analysis of the leaves of the albino tea cultivar ‘Anji Baicha’ identified differentially accumulated proteins in three developmental stages [17]. However, this study was conducted using gel-based approaches, which limited the

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detection of the leaf proteome. Protein detection by 2-DE is biased towards hydrophilic proteins with higher abundance. Thus, identification of hydrophobic

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membrane proteins in the photosynthesis apparatus is difficult [34, 44]. Moreover, by using 2-DE, highly expressed proteins can mask low-abundance proteins. Compared to 2-DE, the advantages of the iTRAQ technique include higher coverage, higher accuracy, and the ability to detect low-abundance proteins and highly hydrophobic membrane proteins [45, 46]. In this study, using iTRAQ to characterize the proteome

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of the tea plant, 3131 unique proteins were identified. Using 2-DE, only 26 proteins, which varied markedly in different albescent stages, were identified in ‘Anji Baicha’

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[18]. The 26 proteins were involved in metabolism of carbon, nitrogen and sulfur; photosynthesis; protein processing; stress defense; and RNA processing. Most of

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these pathways were also mapped by the differentially accumulated proteins identified in our study (Fig. 4C). Additionally, compared to the 2-DE study, our study identified more proteins that are potentially associated with the phenotypes of albino or chlorina

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tea plant cultivars.

Chl and carotenoids are the principal pigments that trap energy in the form of light. Altered Chl metabolism is one of the most important factors leading to the albino or chlorina leaf phenotype. Several studies have reported that the phenotypes of many leaf color mutants with Chl deficiencies are caused by altered expression of genes involved in Chl biosynthesis [11, 15, 47-49]. Pigment determination assays revealed that ZH1 exhibits reduced Chl a and Chl b content. Thus, changes in Chl content may be one cause of the chlorina phenotype. In agreement with this observation, we found that ten genes and four proteins involved in the Chl metabolic process exhibited altered expression levels in ZH1 (Fig. 5A). Different expression levels of the genes and proteins involved in the Chl biosynthesis pathway may therefore reduce Chl content and produce the leaf color phenotype in ZH1. 12

ACCEPTED MANUSCRIPT Photosynthesis takes place in the chloroplast; therefore, abnormal development of the chloroplast and altered Chl biosynthesis may affect the photosynthetic capacity of ZH1 plants (Fig. 1). The differentially accumulated genes and proteins identified in

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our study were significantly enriched in the photosynthesis pathway (Fig. 3B and 4C,

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Table 2). PSI, PSII and cytochrome b6/f are involved in the light reaction, which is the initial stage of photosynthesis. The altered expression of photosynthesis-related genes and proteins in ZH1 may be due to abnormal chloroplast development and

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decreased Chl content. The LHCs play an important role in light harvesting in PSI and PSII. Decreased expression of LHC proteins resulted in impaired stacking of grana in

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the chloroplasts of Arabidopsis [12]. The down-regulation of LHC genes may result in the abnormal chloroplast development and decreased Chl content observed in ZH1. The chlorina phenotype of ZH1 may result from the deficiency of photosynthesis proteins, Chl genes, and Chl proteins.

In our study, differentially expressed genes and proteins could be mapped to the

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following pathways: ‘phenylpropanoid biosynthesis,’ ‘glutathione metabolism,’ ‘phenylanine metabolism,’ ‘photosynthesis,’ and ‘flavonoid biosynthesis.’ This result

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indicates that these pathways may be related to the altered phenotype of ZH1. Changes in the expression of genes and/or proteins involved in the metabolism of

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amino acids and secondary metabolites may account for the altered levels of amino acids and secondary metabolites (e.g., catechins and flavonoids) in ZH1. Therefore,

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future work will examine the metabolic profile of the chlorina tea plant. Our results indicated that ZH1 plants had abnormal chloroplast development, decreased Chl and catechin content, and increased amino acid and theanine content compared to LJ43. This observation was in agreement with other studies of chlorina tea plant cultivars [7, 8]. Also in agreement with this study, Wang (2014) analyzed differentially expressed genes in the chlorina tea cultivar ZH2 and found that the differentially expressed genes could be mapped to the ‘theanine biosynthesis,’ ‘Chl biosynthesis,’ and ‘flavonoid biosynthesis’ pathways. By analyzing these studies on albino or chlorina tea plant cultivars (e.g., ZH1, ZH2, and ‘Anji Baicha’), we can conclude that Chl deficiency, increased amino acid content, and decreased catechin content in the tea leaves are caused by aberrant photosynthesis and altered metabolism of pigments, amino acids, and flavonoids [8, 18]. These may be the common features of albino or chlorina tea plant cultivars. Previous studies, however, are lacking 13

ACCEPTED MANUSCRIPT thorough proteomic characterization of the chlorina tea plant, which is necessary because protein expression levels cannot be predicted from mRNA data. The present study addresses this gap by providing proteomic data for the chlorina tea plant. Our

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results support the findings of a study by Chu (2014), who analyzed Brassica napus

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leaves by iTRAQ and reported that differentially accumulated proteins in a Chl-deficient mutant were involved in photosynthesis; porphyrin and Chl metabolism; and

biosynthesis

of

secondary

metabolites

[34].

By

identifying

ten

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photosynthesis-related proteins that are present at lower quantities in ZH1 compared to LJ43, this study contributes to our mechanistic understanding of the molecular

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basis of the chlorina phenotype (Table 2).

Although there are many similarities between the ZH1 and ZH2 phenotypes [8], we observed some differences between the two cultivars. ZH1 exhibited yellow mature leaves and fresh leaves, while ZH2 exhibited only yellow fresh leaves but not yellow mature leaves (Supplementary data 5). Transcriptomic analysis identified 1009

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differentially expressed genes between ZH1 and ZH2 in fresh leaves (Supplementary data 6A). Two hundred and fifty-seven differentially expressed genes were mapped to

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KEGG pathways, and among these pathways, 11 pathways were enriched with more than 10 differentially-expressed genes (Supplementary data 6B). Identification of the

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1009 differentially expressed genes may provide insight into the molecular mechanism underlying the differences between ZH1 and ZH2.

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Transcriptomic and proteomic analysis provide a wealth of information that may contribute to a better understanding of molecular mechanisms that underlie phenotypes of interest. However, due to the partial proteomic data, the integration of the two analyses may be limited and the analyses may be biased. Most integrative studies have failed to find a correlation or only observed a weak correlation between transcriptomic and proteomic data [50]. The poor correlation was due to various biological factors that affect the translational process and the inadequacy of available statistical tools to compensate for biases in the data collection methodologies [51]. This may explain the discrepancies between transcriptional and posttranscriptional levels of some genes in our study. ZH1 and LJ43 do not share the same genetic background; therefore, the differences that we identified in this study require further investigation. In spite of that, based on our previous work with ZH2, we hypothesize that the differences between ZH1 and LJ43 result from changes in gene and protein 14

ACCEPTED MANUSCRIPT levels. In this study, our transcriptomic and proteomic analysis of ZH1 and LJ43 identified 5944 differentially expressed genes and 437 differentially accumulated proteins.

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However, because the tea plant genome has not been fully sequenced, we cannot rule

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out the possibility that some of the probes and peptides may be from the same gene or protein. This study provides the first proteomic analysis of the chlorina tea plant by iTRAQ and provides new insights into the molecular mechanism of the chlorina

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phenotype on a posttranscriptional level. The data identified many pathways potentially associated with the ZH1 phenotype. Further gene functional analysis is

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necessary to understand the transcriptional mechanism that underlies this phenotype.

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ACCEPTED MANUSCRIPT Abbreviations 2-DE: two-dimensional electrophoresis; ANR: anthocyanidin reductase; Chl: chlorophyll; Chl a: chlorophyll a; Chl b: chlorophyll b; CHS: chalcone synthase; EC:

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enzyme commission; FC: fold change; FDR: false discovery rate; FLS: flavonol

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synthase; GEO: gene expression omnibus; GO: gene ontology; iTRAQ: Isobaric tags for relative and absolute quantitation; KEGG: kyoto encyclopedia of genes and genomes; PCR: protochlorophyllide reductase; qRT-PCR: quantitative real-time PCR;

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SCX: strong cation exchange; TEM: transmission electron microscopic.

Conflict of interest

The authors declare that they have no conflict of interest.

Acknowledgements

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This work was supported by the Earmarked Fund for China Agriculture Research System (CARS-23), the Major Project for New Agricultural Varieties Breeding of

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Zhejiang Province (2012C2905-3), the Project for Issues Concerning Agriculture, Countryside and Farmers of Zhejiang Province (2013-11), the Natural Science

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Foundation of Zhejiang Province (LY14C160001), and the Chinese Academy of Agricultural Sciences through an Innovation Project for Agricultural Sciences and

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Technology（CAAS-ASTIP-2014-TRICAAS）.

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Tables

ZH1 vs. LJ43b

Pathwayc

CUST_7061_PI428262022 CUST_1783_PI428262022 CUST_6359_PI428262022 CUST_20560_PI428262022 CUST_16628_PI428262022 CUST_13726_PI428262022 CUST_7748_PI428262022 CUST_24869_PI428262014 CUST_135_PI428262014 CUST_41899_PI428262014 CUST_36246_PI428262022 CUST_18807_PI428262022 CUST_16799_PI428262022

-13.38 -5.09 -5.78 -7.00 -15.73 -5.37 -9.70 -24.71 -5.74 -8.95 -7.44 -7.89 -10.30

beta-Alanine metabolism Cysteine and methionine metabolism Cysteine and methionine metabolism Cysteine and methionine metabolism Cysteine and methionine metabolism Glycine, serine and threonine metabolism Glycine, serine and threonine metabolism Glycine, serine and threonine metabolism Lysine biosynthesis Lysine biosynthesis Tyrosine metabolism Tyrosine metabolism Tyrosine metabolism

CUST_49329_PI42826202

-12.03

Tyrosine metabolism

CUST_58135_PI428262014 CUST_45272_PI428262014 CUST_33985_PI428262014 CUST_13310_PI428262022 CUST_4155_PI428262022 CUST_13942_PI428262022 CUST_36203_PI428262022 CUST_11244_PI428262014

-13.15 -15.40 -19.07 -19.65 -206.71 -5.58 -6.67 -8.79

Tyrosine metabolism Tyrosine metabolism Tyrosine metabolism Tyrosine metabolism Tyrosine metabolism Valine, leucine and isoleucine degradation Valine, leucine and isoleucine degradation Valine, leucine and isoleucine biosynthesis

CUST_43513_PI428262014

-13.75

Valine, leucine and isoleucine biosynthesis

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Probe namea

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Table 1 Selected genes mapped to pathways related to the ZH1 phenotype, with a 5-FC in transcript abundance in ZH1 compared to LJ43.

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Amino acid metabolism

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Descriptiond

beta-ureidopropionase homocysteine S-methyltransferase 1,2-dihydroxy-3-keto-5-methylthiopentene dioxygenase DNA (cytosine-5-)-methyltransferase DNA (cytosine-5-)-methyltransferase glycine hydroxymethyltransferase glycine hydroxymethyltransferase homoserine kinase diaminopimelate decarboxylase diaminopimelate decarboxylase alcohol dehydrogenase alcohol dehydrogenase alcohol dehydrogenase S-(hydroxymethyl)glutathione dehydrogenase / alcohol dehydrogenase tyrosine decarboxylase tyrosine decarboxylase alcohol dehydrogenase, propanol-preferring tyrosine decarboxylase alcohol dehydrogenase 2-oxoisovalerate dehydrogenase E1 component, alpha subunit 2-oxoisovalerate dehydrogenase E1 component, alpha subunit acetolactate synthase I/III small subunit pyruvate dehydrogenase E1 component subunit alpha aminotransferase

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CUST_48426_PI428262014

5.21

CUST_34330_PI428262022

9.81

CUST_8063_PI428262014

8.79

CUST_6260_PI428262022

12.11

CUST_13282_PI428262022 CUST_43241_PI428262022 CUST_44154_PI428262014 Pigment metabolism CUST_184_PI428262014 CUST_39428_PI428262022 CUST_874_PI428262014 CUST_1809_PI428262014 CUST_25958_PI428262022 CUST_19883_PI428262014 CUST_6689_PI428262014 CUST_10616_PI428262022 CUST_13186_PI428262022 Flavonoid metabolism CUST_18053_PI428262022

5.17 7.13 5.78

Alanine, aspartate and glutamate metabolism; Glycine, serine and threonine metabolism Alanine, aspartate and glutamate metabolism; Arginine and proline metabolism Cysteine and methionine metabolism; Arginine and proline metabolism Cysteine and methionine metabolism; Arginine and proline metabolism Glycine, serine and threonine metabolism Lysine biosynthesis Valine, leucine and isoleucine degradation

-5.49 -5.67 -7.09 -119.60 7.52 7.88 8.74 202.27 -10.62

Porphyrin and chlorophyll metabolism Porphyrin and chlorophyll metabolism Porphyrin and chlorophyll metabolism Porphyrin and chlorophyll metabolism Porphyrin and chlorophyll metabolism Porphyrin and chlorophyll metabolism Porphyrin and chlorophyll metabolism Porphyrin and chlorophyll metabolism Carotenoid biosynthesis

glutamate-1-semialdehyde 2,1-aminomutase uroporphyrinogen decarboxylase uroporphyrinogen decarboxylase chlorophyllase chlorophyllide a oxygenase chlorophyllide a oxygenase chlorophyllide a oxygenase geranylgeranyl reductase xanthoxin dehydrogenase

Flavonoid biosynthesis Flavonoid biosynthesis; Flavone and flavonol biosynthesis Flavonoid biosynthesis Flavonoid biosynthesis Flavonoid biosynthesis Flavonoid biosynthesis Flavonoid biosynthesis Flavonoid biosynthesis

shikimate O-hydroxycinnamoyltransferase

CUST_18451_PI428262022

-5.15

CUST_48467_PI428262022 CUST_32711_PI428262014 CUST_43981_PI428262022 CUST_40733_PI428262022 CUST_920_PI428262014 CUST_15969_PI428262022 Photosynthesis

-5.36 -6.60 -6.85 -100.87 6.08 12.93

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-5.11

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alanine-glyoxylate transaminase / (R)-3-amino-2-methylpropionate-pyruvate transaminase glutamine synthetase S-adenosylmethionine decarboxylase S-adenosylmethionine decarboxylase D-3-phosphoglycerate dehydrogenase diaminopimelate decarboxylase aldehyde dehydrogenase (NAD+)

flavonoid 3'-monooxygenase naringenin 3-dioxygenase flavonol synthase leucoanthocyanidin reductase naringenin 3-dioxygenase leucoanthocyanidin reductase leucoanthocyanidin reductase

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6.11

Photosynthesis

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CUST_2749_PI428262014

light-harvesting complex II chlorophyll a/b binding protein 2 light-harvesting complex II chlorophyll a/b binding protein 1 light-harvesting complex I chlorophyll a/b binding protein 2 F-type H+-transporting ATPase subunit b

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Photosynthesis - antenna proteins Photosynthesis - antenna proteins Photosynthesis - antenna proteins Photosynthesis

Probe names in the custom microarray [29].

b c

-5.25 -7.00 -33.06 6.01

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CUST_1722_PI428262014 CUST_2914_PI428262014 CUST_47530_PI428262014 CUST_16895_PI428262022

F-type H+-transporting ATPase subunit b

Fold differences in transcript abundance between ZH1 and LJ43 leaves. Significance was determined by a P-value of < 0.05.

Genes mapped to pathways according to the KEGG database.

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Description of the genes.

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Genes/Proteins

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Potosystem II PsbB -1.53 -2.52 PsbK -1.58 / PsbO -2.20 / PsbP -1.54 / PsbQ -2.58 / PsbR -2.71 / PsbS 2.11 / PsbW -2.09 / PsbY -1.81 / Psb27 -1.53 / Psb28 1.73 / Potosystem I PsaA / -1.64 PsaB -1.71 -2.06 PsaD / -1.79 PsaE 1.60 -1.85 PsaF -1.73 -2.22 PsaH -2.21 / PsaJ 1.53 / PsaK -1.70 / PsaO -2.39 / Cytochrome b6/f complex PetA -1.9 -1.38 PetC -2.74 / Photosynthetic electron transport PetE -2.25 / PetF 2.19 4.883 PetH -1.88 / PetJ 1.77 / Light-harvesting chlorophyll protein complex (LHC) Lhca1 -2.93 / Lhca2 -10.04 / Lhca3 -1.66 -2.86 Lhca4 -2.12 / Lhcb1 -2.43 / Lhcb2 -5.25 / Lhcb3 -3.39 / Lhcb4 -2.15 -5.10 Lhcb5 -2.2 / Lhcb7 2.86 / a Fold differences in transcript abundance between ZH1 and LJ43 leaves. Significance was determined by a P-value of < 0.05. b

Fold differences in protein abundance between ZH1 and LJ43 leaves. Significance was

determined by a P-value of < 0.05.

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ACCEPTED MANUSCRIPT Figure Legends Fig. 1 Growth performance, chloroplast ultrastructure, and pigment content (chlorophyll and carotenoid) of LJ43 and ZH1. (A, B) Growth performance of

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LJ43 (A) and ZH1 (B) shoots during their sprouting period in the spring. (C, D)

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Chloroplast ultrastructure in the young leaves of LJ43 (C) and ZH1 (D), Ch: chloroplast, CW: cell wall, Gr: grana, Th: thylakoid. (E) Chlorophyll a (Chl a), chlorophyll b (Chl b), Chl a+b, lutein, and β-carotene content of two leaves and one

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bud of LJ43 and ZH1. Significant differences between ZH1 and LJ43 are indicated

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with two asterisks (P <0.01).

Fig. 2 Theanine, amino acid, and catechin content of two leaves and one bud of LJ43 and ZH1. Two leaves and one bud of LJ43 and ZH1 were sampled during sprouting time in the spring. Significant differences between ZH1 and LJ43 are indicated with two asterisks (P <0.01).

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Fig. 3 Differential expression analyses. (A) Diagram showing the number of significantly differentially expressed genes (P-value < 0.05, FC > 2) in ZH1 compared

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with LJ43. Differentially expressed genes related to the different phenotype and biochemical traits of ZH1 are showed in blue oval. (B) Significantly (P-value < 0.05,

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FDR < 0.05) enriched pathways (based on KEGG) among the 5,944 differentially expressed genes.

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Fig. 4 Gene ontology (GO), Cluster of Orthologous Groups of proteins (COG), and KEGG pathways analysis of differentially accumulated proteins in two leaves and one bud of LJ43 and ZH1. (A) GO classification of the differentially accumulated proteins. (B) COG classification of the differentially accumulated proteins. (A: RNA processing and modification; B: Chromatin structure and dynamics; C: Energy production and conversion; D: Cell cycle control, cell division, chromosome partitioning; E: Amino acid transport and metabolism; F: Nucleotide transport and metabolism; G: Carbohydrate transport and metabolism; H: Coenzyme transport and metabolism; I: Lipid transport and metabolism; J: Translation, ribosomal structure and biogenesis; K: Transcription; L: Replication, recombination and repair; M: Cell wall/membrane/envelope biogenesis; O: Posttranslational modification, protein turnover, chaperones; P: Inorganic ion transport and metabolism; Q: Secondary metabolites biosynthesis, transport and catabolism; R: General function 24

ACCEPTED MANUSCRIPT prediction only; S: Function unknown; T: Signal transduction mechanisms; U: Intracellular trafficking, secretion, and vesicular transport; Z: Cytoskeleton). (C) Pathways enrichment analysis of the differentially accumulated proteins. 5

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representation

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significant

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posttranscriptional changes involved in the Chl biosynthesis and flavonoid biosynthesis pathways. (A) Genes and proteins involved in the ‘chlorophyll biosynthesis’ pathway. (B) Genes and proteins involved in the ‘flavonoid biosynthesis’

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pathway. The numbers represent the FC; blue and red numbers indicate microarray and iTRAQ data, respectively; positive numbers indicate significantly (P < 0.05)

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up-regulated genes or proteins, while negative numbers indicate significantly (P < 0.05) down-regulated genes or proteins. ‘/’ indicate not significant or not present in the dataset.

Fig. 6 Quantitative RT-PCR validation. Transcript abundance of 14 selected genes that showed differential expression both on transcriptional and posttranscriptional

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levels were determined by qRT-PCR analysis of ZH1 and LJ43 leaves. All data are the

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mean ± SD (n=3).

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ACCEPTED MANUSCRIPT Biological significance Chlorina tea plant breeding has recently become a topic of great interest. Chlorina plant leaves exhibit altered levels of chloropyll, carotenoid, catechin, and amino acids,

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all of which are important determinants of the quality of tea and its health effects.

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However, partially due to insufficient proteomic data, there is very limited understanding of the underlying mechanism for this altered phenotype. Here, we present a combined physiological, transcriptomic, and proteomic analysis that reveals

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several important pathways associated with the chlorina tea plant phenotype. This integrative analysis provides a better understanding of the molecular mechanisms that

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of new, high-quality tea cultivars.

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are responsible for the chlorina phenotype and may have applications in the breeding

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ACCEPTED MANUSCRIPT Highlights: We identified 3131 proteins from Camellia sinensis leaves using the iTRAQ approach.

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Comparative transcriptomics and proteomics of two tea cultivars were performed.

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Differentially expressed proteins/genes enriched in pathways related to phenotype.

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We clarified the mechanism of chlorina through microarray and proteomics approach.

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