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Proteome Analyses Using iTRAQ Labeling Reveals Critical Mechanisms in Alternate Bearing Malus prunifolia Sheng Fan, Dong Zhang, Chao Lei, Hongfei Chen, Libo Xing, Juanjuan Ma, Caiping Zhao, and Mingyu Han J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00357 • Publication Date (Web): 23 Aug 2016 Downloaded from http://pubs.acs.org on August 24, 2016

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Journal of Proteome Research

Proteome Analyses Using iTRAQ Labeling Reveals Critical Mechanisms in Alternate Bearing Malus prunifolia Sheng Fan1, Dong Zhang1, Chao Lei, Hongfei Chen, Libo Xing, Juanjuan Ma, Caiping Zhao, Mingyu Han* College of Horticulture, Northwest A&F University, Yangling, Shaanxi, China 1

These authors contributed equally to this work.

*

Corresponding author:

E-mail: [email protected] Tel.: 86-029- 87081635 Fax: 86-029- 87081635

1

ACS Paragon Plus Environment


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ABSTRACT Alternate bearing (AB) trees, including Malus prunifolia, are characterized by alternating cycles of heavy (ON tree) and low (OFF tree) fruit loads. The mechanisms regulating the AB phenomenon have not been fully characterized. We completed an iTRAQ-based investigation of M. prunifolia to identify the proteome and metabolic differences between the leaves of ON and OFF trees. We identified 667 differentially expressed proteins, and they influenced multiple biochemical pathways, including photosynthesis, carbohydrate metabolism, glycolysis, protein processing, redox activities,

and

secondary

metabolism.

Bioinformatics

analyses

indicated

photosynthesis was the most significant biological process affecting the AB. We observed that 47 photosynthetic proteins affecting photosystem I and II reaction centers, cytochrome b6/f complex, electron transport, and light-harvesting chlorophyll were less abundant in ON tree leaves than in OFF tree leaves. Additionally, physiological analyses validated the potential metabolic activities. Nitrogen and phosphorus contents were significantly higher in ON tree leaves, while potassium levels were lower. Starch content, ZR, GA4+7 levels, and flower control gene expression levels (i.e., MdFT1, MdLFY, MdAP1, and MdSPL9) were lower in ON tree leaves than in OFF tree leaves, suggesting they affected the AB phenotype. Our findings help further investigate on the photosynthesis as well as other processes in AB. Those identified DEPs and important biological processes can be useful theoretical basis and provide new insights into the molecular mechanisms regulating AB in perennial woody plants. 2


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KEY WORDS Malus prunifolia, alternate bearing, proteomics, iTRAQ

INTRODUCTION Malus prunifolia is native to Shaanxi province, China, and it has a long history of being grown as an important apple species in China. It is a hardy tree that is tolerant to various abiotic stresses, such as drought, water, and salinity stresses. It is widely grown in northern and northwestern China, as well as in Liaoning and Shandong provinces. Recently, M. prunifolia trees have been used as rootstocks. Despite their advantages, M. prunifolia trees exhibit an alternate bearing (AB) fruit production phenotype. Apple yield is one of the most important characteristics for apple breeding. Alternate bearing refers to a cycle between high (ON tree) and low (OFF tree) fruit yields, and has been detected in evergreen and deciduous trees. In ON trees, developing fruits inhibit vegetative growth, leading to a lower abundance of flower-bearing shoots and nodes in the following spring. In contrast, OFF trees exhibit an increased return bloom during the following spring.

1-2

Additionally, ON

trees are characterized by the presence of many flowers; while OFF trees have fewer flowers, but increased vegetative growth. Moreover, in some cases, consecutive seasons of low fruit yields can occur, resulting in considerable economic losses. Extensive cross-talk mechanisms have been associated with the AB phenomenon. Additionally, researchers have studied AB fruit crops for several decades, but questions remain regarding the underlying regulatory mechanisms.1, 3-7 Fruit load may 3



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be affected by many developmental stages, such as flower induction, transition, and development, as well as during bud break. 2 The flowering initiation is a key stage for crops, and can be inhibited by excess fruit production in a previous season. The induction of flowering has been investigated in terms of plant nutrition and hormones, and it was determined that carbohydrate sources and sinks are important for flowering in Arabidopsis thaliana.8 Developing fruits need more photoassimilate sinks, and the depletion of photoassimilates inhibits flowering.8-9 Carbohydrate levels have been adjusted through chemical and manual processes, and fruit thinning of ON trees may influence the AB process, thereby increasing the flowering rate in the following year. 10

Hormones, including gibberellin (GA), abscisic acid (ABA), auxin (IAA), and

cytokinin, affect the flowering of perennial plants. Auxin stimulates GA synthesis in the meristem, and auxin and GA can inhibit floral development in fruit trees. 11-12 The ABA levels are lower in the buds of OFF citrus trees than in the buds of ON trees. 12 Additionally, the application of exogenous cytokinin promotes flowering in apple trees, 13 while the effects of other plant hormones have not been fully characterized.14-17 Furthermore, experiments involving Spencer seedless apples revealed that seed development was more important than nutrition for the AB phenotype.18-19 However, investigations of hormones and nutrient demands during flowering are necessary because some questions remain, especially dealing with AB. Environmental conditions (e.g., temperature, water level, and exposure to stresses) and internal factors (e.g., hormones) can influence flower induction in Arabidopsis thaliana. Additionally, genetics-based studies have been completed on 4


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fruit trees. For example, transcriptomics investigations identified important genes and miRNAs involved in flowering in apple, 11, 20 citrus, 1, 12 pear ,21-22 and peach23 trees. The generated data has led to a better understanding of floral induction and differentiation processes. Moreover, genes associated with floral meristem identity and floral integrator genes have also been characterized in woody plants. Ectopically expressed MdFT1, MdFT2, Md-miRNA156h and Md-miR172e can alter flowering time in Arabidopsis thaliana. 24-27Additional, the transgenic apple trees showed precocious flowering after silencing the MdTFL1.In addition, fruit load also influences the expression of genes in leaves and buds, including FLOWERING LOCUS T (FT), which is a major component of florigen, SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1), APETALA1 (AP1), LEAFY (LFY), and SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) in citrus AB trees 1, 9-10, as well as in avocado 17and mango 28 trees. These transcriptional levels still have limitations for investigating the AB on protein expression. 11, 20 Leaves are important vegetative organs that are often independently studied when analyzing various floral biologies.7, 29-31 The AB phenotype involves complex metabolic and molecular processes that have not been comprehensively characterized. Proteome-level investigations have provided new insights into post-transcriptional modifications and cellular functions of proteins.32 They have enabled analyses of global protein expression patterns influencing critical biological processes. These studies have also clarified data generated from transcriptome-level investigations, and provided direct insights into metabolic processes.33-35The two-dimensional 5



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electrophoresis has been used for protein separation and analysis, while the spectrometry-based iTRAQ allows the quantification of protein abundance under four or eight phenotypes with high quantification. And it also has advantages of high resolution precursor ion section and peptide fractionations. 34 Proteomics technologies have been used to assess the agronomic characteristics of fruit trees, such as drought stress tolerance in apple, 36 anthocyanin biosynthesis in mango, 37 fruit loading in mandarin, 7 fruit softening in peach, 38 and bud sports in pear. 39 And it was also used to in other plants .40-42 However, the whole proteomes have not been investigated in AB in apple trees, and this iTRAQ technique has not been used. We employed a high-throughput iTRAQ methodology to examine proteins affecting AB mechanisms. Our results helped to characterize relevant biological processes and identified pivotal candidate proteins potentially involved in regulating the AB phenotype. The present work provided more information as follows: identification the DEPs between AB; comparing their different expressions; analysis their different biological processes and revealing some important mechanisms. Our findings indicated that various pathways affect the AB process, including those related to metabolism, glycolysis, protein processing, redox activities, and secondary metabolism especially the photosynthesis and carbohydrate. Those most DEPs involved in photosynthesis can be useful for better understanding of the mechanisms in AB. Our proteomic data, combined with bioinformatics and physiological analyses, were used to characterize the proteomes of AB fruit trees, providing a better understanding of their biology and new insight. 6


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MATERIALS AND METHODS

Plant Materials We used a nearly 100-year-old M. prunifolia tree growing in Fuping, Shaanxi province, China (34° 47′N, 109°2′E). This only one tree shows obvious AB phenotype (Figure 1). The two main branches that had developed across the trunk were good materials, which were able to reduce the background noise resulting from external factors (Figure 1). The number of flowers of the two branches fluctuated different between high and low in alternating years. One branch characterized little flowers and fruits while the other showed more flowers and fruits, which would be alternated year by year (Figure 1C, D). Leaves adjacent to flower buds were randomly collected at 9:00 on a clear morning every 2 weeks from 1 day after full blossom (DAFB) to 90 DAFB. Proteins were extracted from leaves collected 45 DAFB, which is an important flower induction period according to our previous study. 13 Samples were immediately stored in ice bags and liquid nitrogen for subsequent physiological and molecular assessments.

Morphological Assessment and Photosynthetic Parameters Leaves were scanned using an Epson Perfection V300 photo scanner (Seiko Epson Corp., Nagano, Japan), and leaf area was calculated using Adobe Photoshop CS4 software (Adobe System, San Jose, CA, USA). The fresh weight of 90

7



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ethanol-washed leaves was determined before the leaves were dried at 60 °C for 3 days for dry weight measurements.

The LI-6400T portable photosynthesis system (Li-Cor, Lincoln, NE, USA) was used for in vivo measurements of photosynthetic parameters of samples collected at 45 and 90 DAFB on sunny days between 9:00 and 11:00 am. We analyzed net photosynthetic efficiency (Pn), stomatal conductance (Gs), and CO2 concentration (Ci). Leaves were illuminated with a 6400-02B light source at a saturating incident photosynthetic photon flux density of 1000 µmol/m2/s from 670-nm red light-emitting diodes with 10% blue light.

Determination of Soluble Nitrogen, Phosphorus, and Potassium Contents Soluble sugar contents in leaves (approximately 500 mg dry weight) harvested from ON and OFF trees was determined using a high-performance liquid chromatography (HPLC) (Waters 2414, Visible Detector, Shaanxi, China). The detector oven and column heater module temperatures were 40 °C and 60 °C, respectively. Carbohydrates were eluted with 50 mg/L calcium ethylenediaminetetraacetic acid (EDTA-Ca) at a flow rate of 0.5 mL/min. After extracting soluble sugars, the tissue residue was dried and used to measure starch content. 43 The Kieldahl method with an automated nitrogen analyzer (Kjektec System FOSS-2300, Sweden) was used to determine nitrogen levels. 44 The continuous flow auto analyzer was used to measure phosphorus and potassium concentrations as 8


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previously described. 43, 45-47

Determination of Endogenous Hormone Contents Differences in the endogenous hormone contents between the leaves harvested from ON and OFF trees were determined using an enzyme linked immunosorbent assay (ELISA).

48

Approximately 0.4 g samples were ground in liquid nitrogen, and then

treated with cold 80% (v/v) methanol and 1 mM butylated hydroxytoluene overnight at 4 °C. Extracts were centrifuged at 10,000 × g (4 °C) for 20 min, passed through a Sep-Pak C18 cartridge, and dried under N2. The resulting residues were dissolved in phosphate buffer. The ELISAs used to analyze zeatin riboside (ZR), ABA, IAA, and GA contents were completed in 96-well microtitration plates. After adding standard hormones, samples, and antibodies, the coated plates were incubated for 40 min at 37 °C. After rinsing plates four times, 100 µL peroxidase-labeled goat anti-rabbit immunoglobulin was added to each well, and the plates were incubated for 40 min at 37 °C. Colored substrate (o-phenylenediamine) was added to each well, and the reaction was halted by the addition of 3 M H2SO4. Absorbances at 490 nm were determined with an ELISA spectrophotometer and used to calculate ZR, ABA, IAA, and GA concentrations. All antibodies against each hormone were monoclonal and were obtained from the Center of Plant Growth Regulator, China Agricultural University.

Protein Extraction and Preparation 9



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Leaves from ON and OFF trees (approximately 500 mg) were ground to a fine powder in liquid nitrogen, and then treated with lysis buffer (7 M urea, 2 M thiourea, 4% CHAPS, 40 mM Tris-HCl, pH 8.5) containing 1 mM phenylmethylsulfonyl fluoride and 2 mM EDTA. After 5 min, 10 mM dithiothreitol (final concentration) was added. The suspensions were all kept in ice and sonicated at 200 W for 15 min, and then centrifuged at 30,000 × g (4 °C) (Eppendof, 5430R) for 15 min. Supernatants were transferred to a new tube and treated with 10 mM dithiothreitol (final concentration). The samples were incubated at 56 °C for 1 h, and then treated with 55 mM iodoacetamide for 1 h in darkness. Supernatants were thoroughly mixed with chilled acetone for 2 h at -20 °C, and then centrifuged at 30,000 × g (4 °C) for 15 min. Pellets were air-dried for 5 min before being dissolved in 500 µL 0.5 M TEAB and sonicated at 200 W for 15 min. Samples were centrifuged at 30,000 × g (4 °C) for 15 min and then stored at -80 °C.

ITRAQ labeling and strong cation exchange Total protein (100 µg) from each sample solution was digested with Trypsin Gold (Promega, Madison, WI, USA) (protein to trypsin weight ratio of 20:1) for 12 h at 37 °C. Peptides were dried by vacuum centrifugation at 4 °C, and then reconstituted and processed in 0.5 M TEAB according to the manufacturer’s protocol for 4-plex iTRAQ analysis (Applied Biosystems). The ON tree samples were labeled with 113 and 115, while the OFF tree samples were labeled with 114 and 118. All samples were mixed and then fractionated using an Ultremex SCX (strong cation exchange) column 10


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(4.6 × 250 mm) and the Shimadzu LC-20AB HPLC system (Shimadzu, Kyoto, Japan). The mixtures were dissolved with 4 mL buffer A (25 mM NaH2PO4 in 25% acetonitrile, pH 2.7) and loaded on to a SCX column containing 5-µm particles (Phenomenex) and eluted (1 mL/min) with buffer A for 10 min, followed by and isocratic 5%-60% buffer B gradient (25mM NaH2PO4, 1 M KCl in 25% acetonitrile, pH 2.7) for 25 min, and then a 60-100% buffer B gradient for 1 min. The system was then maintained at 100% buffer B for 1min. Eluted samples were quantified by absorbance at 214 nm. Finally, peptides were pooled into 12 fractions for LC-ESI-MS/MS analysis.

LC-ESI-MS/MS Analysis The LC-ESI-MS/MS analysis was based on the Triple TOF 5600 system (AB SCIEX, Concord, ON) with a Nanospary III source (AB SCIEX, Concord, ON). The emitter was the pulled quartz tip (New Objectives, Woburn, MA ).The sample fractionation and subsequent liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) analysis were completed using established procedures. 49-51

The fractions were solubilized in buffer A (5% acetonitrile and 1% formic acid)

at a final concentration of 0.5 µg/µL. Samples of 2.5 µg (5 µL) were loaded onto a LC-20AD nanoHPLC (Shimadzu, Kyoto, Japan) with the autosampler (2 cm C18 trap column). Then, peptides were eluted and loaded onto a C18 column (10 cm in length, inner diameter 75 µm, packed in-house) at a flow rate of 8 µL/min for 4 min and chromatographed for 35 min using a gradient of 2 to 35% buffer B (95% acetonitrile 11



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and 1% formic acid) at a flow rate of 300 nL/min, followed by ramping up to 60% buffer B during 5 min, up to 80% for 2 min, maintained for 2 min, and returned to 5% buffer B in 1 min. Mass spectrometric analysis was depended on a data-dependent manner with full scans of the Orbitrap mass analyzer ( resolution:≥30 000 at m/z 400; automatic gain control: 500000 ions). The 30 most intense precursor ions were used for MS/MS fragmentation and detected at a mass resolution of 15000 at m/z 400 with peptides above a 5 count threshold selected and excluded for 30 s of 30 mDa mass tolerance. The fragmentation was activated with higher energy collision dissociation. Full Fourier transform mass spectrometry and MS/MS was set to 1 and 0.1 million ions, with a maximum time of accumulation of 2 s. The general workflow of the iTRAQ experiment is presented in Figure 2.

Protein Identification and Functional Annotation The LC/ESI-MS/MS data were analyzed using the Promote Discoverer 1.3 software (Thermo Fisher Scientific, San Jose, CA, USA) with default parameters to generate peak list. Proteins were identified using the Mascot search engine (version 2.3.02) (Matrix Science, London, UK) against the apple genome protein database (http://www.rosaceae.org) which has 68698 sequences. The Mascot search involved the following parameters: Type of search: MS/MS ion search; Enzyme: trypsin; Fragment mass tolerance: 0.1Da; Mass values: monoisotopic; Variable modifications: Gln to pyro-Glu (N-term Q), Oxidation (M), iTRAQ 4-plex (Y); Peptide mass tolerance: 0.05 Da; Instrument type: Default; Max missed cleavages: 1; Fixed 12


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modifications: Carbamidomethylation (C), iTRAQ 4-plex (N-term), iTRAQ 4-plex (K). The Mascot search engine uses probability-based scoring to determine whether results are significant (www.matrixscience.com/help/scoring_help.html#PBM). Only peptides with a confidence interval ≥ 95% according to their Mascot probability scores were considered to have been identified. Protein quantitation was performed at the

peptide

level

following

an

established

(http://www.matrixscience.com/help/quant_format_help.html).We

procedure

employed

the

target-decoy strategy of Elias and Gygi to estimate the false discovery rate(FDR)， which can be used for measuring the identification certainty.52 The quantitative protein ratios were weighted and normalized against the median ratio for each set of experiments. The manufacturer’s recommended isotope correction factors were applied. We only used ratios with p values < 0.05; and ratios > 1.20 or < 0.83 were considered significant. Gene Ontology was used to functionally classify the differentially expressed proteins (DEPs) (http://www.geneontology.org). The Cluster of Orthologous Groups of proteins (COG) is a database for classifying protein orthologs, and was also used to evaluate potential protein functions. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/) was used to clarify the pathways the DEPs may be associated with.

Immunoblotting Analysis Protein levels were analyzed in leaves harvested from ON and OFF trees at 30, 45 and 13



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60 DAFB. The separated proteins were electrophoretically transferred to a PVDF membrane (Millipore) and the membrane was blocked with 5% (v/v) skim milk in 0.05 % (w/v) Tween-20 Tris-buffered saline. Cytb6 and PsbB levels were probed with commercial polyclonal antibodies (Agrisera, Sweden).The antibodies were diluted with 1:2000 and incubated at 4 °C overnight. The membrance was washed with 0.05% (w/v) Tween-20 Tris-buffered saline and incubated with the the secondary antibody (horseradish peroxidase-conjugated anti-rabbit) at 37 °C for 1 h. Bound antibodies were visualized using ECL reagents (Amersham, Pharmacia Biotech, Uppsala, Sweden).

Gene Expression Analysis The mRNA levels of key flowering control genes were compared between leaves harvested from ON and OFF trees at all stages. Two FT genes, SOC1, LFY, AP1, and SPL9 were selected based on previous studies. 20,53Additionally, mRNA levels of DEPs were also analyzed. Total RNA was extracted according to a modified cetyltrimethylammonium bromide-based method as previously described.54 First-strand cDNA was synthesized from 1 µg total RNA using the PrimeScript RT Reagent Kit with gDNA Eraser (TaKaRa Bio, Shiga, Japan) following the manufacturer’s instructions. Each quantitative reverse transcription polymerase chain reaction (qRT-PCR) mixture contained 10.0 µL SYBR Premix Ex Taq (Takara, Ohtsu, Japan), 0.4 µL primer (10 µM), 2 µL cDNA, and 7.2 µL RNase-free water for a final volume of 20 µL. The qRT-PCR was completed using a LightCycler 1.5 instrument 14


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(Roche, Germany) and the following program: 95 °C for 30 s; 40 cycles of 95 °C for 5 s and 60 °C for 20 s. A template-free control for each primer pair was included in each run. The qRT-PCR primers (Table S1) were designed using Primer 3 software (Genetyx Software, version 10). All qRT-PCR data were normalized using the threshold cycle value for the apple EF-1α gene (GenBank accession: DQ341381; Table S1). All samples were analyzed three times.

Statistical Analysis One-way analysis of variance with Tukey–Kramer multiple comparison tests was performed using DPS software version 7.0 (Zhejing University, Hangzhou, China) for morphological indexes, photosynthetic parameters, sugars, hormones, and Q-PCR results.

RESULTS Leaf Morphology during Flower Induction To analyze the phenotypes of leaves collected from the ON and OFF trees, temporal changes in leaf morphology were assessed. Fresh weight, dry weight, and leaf area increased significantly at 15 DAFB for ON and OFF tree leaves, with slight increases afterward. Obvious differences were observed between ON and OFF tree leaves in terms of fresh weight (Figure 3A), dry weight (Figure 3B), and area (Figure 3C). The fresh and dry weights of ON tree leaves were lower than those of OFF tree leaves. Additionally, the leaf phenotypes were completely different between the ON (Figure 15



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1A) and OFF (Figure 1B) trees, with significantly bigger leaves on the OFF tree (Figure 3C).

Physiological Evaluation of AB Trees Soluble sugars (i.e., sucrose, glucose, fructose, and sorbitol), starch, and other plant nutrients (i.e., nitrogen , phosphorus, and potassium) were compared between the leaves of ON and OFF trees at different growth stages (Figure 4). There were significant differences in nitrogen content, with higher levels in ON tree leaves, except on 1 and 90 DAFB (Figure 4A). Additionally, phosphorus content was higher in ON tree leaves at all stages (Figure 4B). In contrast, potassium concentrations exhibited the opposite trends, with lower levels in the leaves of ON trees (Figure 4C). We observed various changes to sugar and starch contents throughout the different growth stages, as indicated in Figure 4D–I. During the early growth period, the sucrose content was higher in OFF tree leaves, but at subsequent stages, it was higher in the ON tree leaves (Figure 4D). Glucose levels were consistently higher in OFF tree leaves than in ON tree leaves, except during the initial growth stage (Figure 4E). Fructose concentrations were initially lower and then higher in ON tree leaves compared with OFF tree leaves (Figure 4F). Changes in sorbitol and total sugar content were always the same, which was because the total sugar content was mostly consisted by sorbitol. The sorbitol and total sugar levels in OFF tree leaves slowly decreased at first, but then began to increase (Figure 4G, H). They were also lower in ON tree leaves than in OFF tree leaves during the early growth stage, but during the 16


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later growth periods, they were similar between the two leaf types. Starch contents were lower in ON tree leaves than in OFF tree leaves (Figure 4I). These results suggested that carbohydrate metabolism in leaves regulated AB at different growth stages through complex networks. Photosynthetic characteristics in leaves collected 45 and 90 DAFB were assessed. The Pn and Gs values were higher in OFF tree leaves than in ON tree leaves, while Ci values exhibited the opposite pattern (Table 1). We simultaneously measured the abundance of five hormones (i.e., ZR, IAA, ABA, GA1+3, and GA4+7) at different growth stages (Figure 5). The ZR and GA4+7 levels were higher in OFF tree leaves than in ON tree leaves, while the GA1+3 and IAA level were higher in ON tree leaves and ABA levels was not always consistent during the development. Additional, the ratio of ZR/GAs was also higher in OFF tree leaves than in ON tree leaves.

Quantitative Proteomic Analysis by iTRAQ Labeling To verify the changes in proteins affecting the AB phenomenon in apple trees, leaf proteins were extracted, and then analyzed by LC/ESI-MS/MS and quantified by iTRAQ. A total of 46,018 spectra were generated, and 27,955 unique spectra were obtained after low scoring spectra were eliminated. We identified 18,586 peptides, and matched 14,168 peptides with known sequences. Finally, 5,467 proteins were identified (Figure 6A). The average molecular mass of the identified proteins was between 20 and 70 kDa (Figure 6B), and amino acid sequence coverage was higher than 5% for 69.72% of the proteins (Figure 6C). The variations of the two biological 17



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replicates were calculated according to their quantitative data and most proteins showed the variation less than 20 % (Figure S1). They all showed little variation between two biological replicates, indicating a better quality and reproducibility of the data.

Detailed information regarding all identified proteins is provided in Table S2. In total, 667 DEPs were identified. We determined that 354 proteins were up-regulated (Table S3) and 313 proteins were down-regulated (Table S 4) in the ON tree leaves relative to the abundances in the OFF tree leaves. Different expression proteins were separated under two biological replications underwent a ratio 1.20, p value 1.50 (p < 0.05). These proteins were mostly associated with energy, metabolism, stress response and defense, cell structure, and protein synthesis and turnover. Some proteins had unknown functions (Table 3).

Immunoblotting Analysis of the Selected DEPs To validate the changes of proteins involved in photosynthesis as found in our proteomic study, we selected two important proteins PetA (Cytochrome b6/complex) and PsbB (PhotosystemⅡ) for immunoblotting analysis . As shown in figure 8, the immunoblotting analysis showed that protein levels were all increased during the period. Additionally, they all displayed lower expression in ON trees than OFF trees 19



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at 30, 45 and 60 DAFB, and the findings were consistent with our iTRAQ data.

Transcriptional Analysis of Selected Differentially Expressed Genes and Flowering Control Genes To assess the correlation between mRNA and protein expression levels, qRT-PCR analyses were completed for energy processing and photosynthesis genes (Table 3). The qRT-PCR data indicated the expression levels of genes related to glycolysis, carbohydrate metabolism, and photosynthesis were consistent with the observed protein abundances (Figure 9). We also examined the expression of genes affecting flowering (Figure 10). The FT1 transcript was more abundant in OFF tree leaves than in ON tree leaves, while FT2 transcript abundance was inconsistent in ON tree leaves than in OFF tree leaves. Additionally, the SOC1 transcript level was higher in ON tree leaves. We observed that the LFY and SPL9 expression levels were similar to that of FT1, while the expression of AP1 was, but was generally first increased and then decreased in ON tree leaves compared with OFF tree leaves.

DISCUSSION Although some mechanisms regulating the AB phenotype have been identified, there are still many uncertainties. 4-5, 9, 12, 15, 17 Our study involved an AB apple tree, and used iTRAQ along with other analyses to provide new information regarding the AB phenotype. We focus our discussion on proteins related to photosynthesis, energy, and 20


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other relevant processes.

Leaf Morphology and Physiology in ON and OFF Trees The AB phenotype was observed in our Malus prunifolia tree (Figure 1), and the leaf samples collected from two branches were suitable materials for investigating the AB mechanism. Leaves are important nutritive organs that control floral induction.55-56 The fresh weight, dry weight, and leaf area were higher in OFF tree leaves than in ON tree leaves at all examined growth stages. These results may be explained by the fact that OFF tree leaves experience little competition from fruits for nutrients. Plants require sufficient amounts of nutrients to maximize yields. Nitrogen, phosphorus, and potassium are the major components of cellular proteins, and are also involved in the metabolism and transport of vitamins, nucleic acids, and carbohydrates. 57-58Adequate nutrient reserves enable the induction of flowering.12-13 We observed higher nitrogen and phosphorus contents in ON tree leaves than in OFF tree leaves (Figure 4A, B), which was ideal for fruit development. Additionally, this higher nitrogen levels are not beneficial for flowering, 53 which was also consistent with the less flower in ON trees. Sugars play important roles during flowering as they are the main energy source. Fluctuating sugar levels influence flower induction and development.53, 59 Sugar may also function as a signaling molecule between leaves and buds. 53, 60 The concentrations of soluble sugars, including sucrose, glucose, fructose, and sorbitol, were lower in ON tree leaves than in OFF tree leaves during the initial growth stages 21



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(Figure 4D–H), likely because the sugars were being consumed by the developing flowers. 8-9 At later growth stages, these soluble sugars exhibited various trends, suggesting sugars affect the AB phenotype and flower induction through different means. Sucrose levels can be good indicators of carbohydrate status in AB fruit trees. Additionally, the lower starch content observed in ON tree leaves may explain the differences between ON and OFF trees (Figure 4I). A lower starch level in ON trees has also been reported in previous studies. 7, 61 The sucrose, glucose, and starch levels in OFF tree leaves gradually increased at later growth stages, and exceeded the corresponding levels in ON tree leaves. This accumulation of sugar in OFF tree leaves may function as a carbohydrate sink. 62 Endogenous hormones have important roles during leaf development and flower induction. 63-64The concentrations of the five examined hormones gradually increased in ON and OFF tree leaves. And the ZR and GA4+7 were higher in the OFF tree leaves than in ON trees. IAA and GA1+3 were higher in the ON tree levels than in OFF trees. Gibberellins inhibit flowering in many perennial woody plants. 11,64Therefore, the higher GA levels in the ON tree leaves resulted in a stronger inhibitory effect, and fewer flowers. Consequently, the tree produced a lower fruit yield in the following year. Additionally, a greater production of fruits in ON trees may lead to increased hormone levels. A previous study concluded that OFF trees or de-fruited trees have lower ABA and IAA levels in citrus buds.12 Similarly, our results indicated that the ABA and IAA contents in OFF tree leaves were lower than that of ON tree leaves.

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Expression of Flowering Control Genes in ON and OFF Tree Leaves Several genes that control flowering have been identified in A. thaliana and other perennial plants. We analyzed the key genes involved in flower induction by qRT-PCR, and determined that the FT1, LFY, AP1, and SPL9 expression levels were affected by fruit load. The transcript levels of FT1, LFY, AP1, and SPL9 were lower in ON tree leaves than in OFF tree leaves, which is similar to the results obtained for the AB mandarin tree. 9-10The FT gene is a pivotal factor for flowering, and FT1 overexpression results in early-flowering phenotypes in transgenic apple trees.24 We observed that the FT expression levels were higher in OFF tree leaves than in ON tree leaves, and this repressed in FT was consistent with avocado.17 Additionally, LFY, AP1, and SPL9 are known to promote flowering .13, 20 Increased expression of FT can promote the expression of SPL9, LFY, and AP1, as indicated in A. thaliana. 66 The SOC1 and FT2 transcript levels were inconsistent during our observation period. This may have been because these genes have redundant functions, especially FT2.67 A similar study showed that these genes were also involved in flower induction and altered in AB avocado and Citrus trees,1, 17 suggesting these genes have important roles in AB apple trees.

Different Biological Processes in ON and OFF Trees A bioinformatics analysis of all proteins in Malus prunifolia leaves uncovered several DEPs (Table S2). For a more thorough characterization of the proteins affecting the AB mechanism, we focused on DEPs with a ratio in expression of >1.50 or < 0.66 23



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(Table 3). A total of 71 significantly affected proteins were classified according to their functions and involvement in biological processes. Of these proteins, 33 were more abundant in ON tree leaves, while 38 were more highly expressed in OFF tree leaves. We discussed the DEPs in greater detail in the following sections.

Photosynthetic Processes Photosynthesis is one of the most important energy-producing processes during plant growth and development. Interestingly, the expression levels of all DEPs were down-regulated in the ON tree leaves (Table S2) according to KEGG analysis. This suggests that photosynthesis might be a critical biological process regulating AB. Three proteins including photosystem I reaction center subunit VI proteins (MDP0000142895 and MDP0000909197) and photosystem II reaction center psb28

protein (MDP0000132802) involved in light harvesting, electron transport, proton gradient formation, and energy production were down-regulated in ON tree leaves. These proteins can capture and transmit light energy, and are also involved in coordinating activities between photosystems I and II, maintaining thylakoid membrane structures, protecting plants from light stress, and responding to various environmental stimuli.68-69 Decreased production of these proteins may reduce the efficiency of electron and NADPH transport, and affect the cell membrane proteins involved in ATP synthesis. This is consistent with the fact that the expression of ATP synthases was down-regulated in ON tree leaves. Additionally, the abundance of several chlorophyll a-b binding proteins (i.e., MDP0000337585, MDP0000757636, 24


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MDP0000269859, MDP0000150729, MDP0000601491 and MDP0000153663), chloroplast stem-loop binding protein (MDP0000838506) and chloroplastic proteins (MDP0000217142 and MDP0000880187) was lower in ON tree leaves than in OFF tree leaves. The expression of these chlorophyll-binding proteins is regulated by light and photosynthetic activity. 68-71 The lower abundance of these proteins in ON tree leaves implies that photosynthesis is more active in OFF tree leaves. Our results suggest that fruit load affects leaf photosynthesis rates. To confirm the photosynthetic differences between ON and OFF tree leaves, we analyzed selected photosynthesis-related leaf characteristics. Our results revealed that ON tree leaves have lower Pn and Gs values, but a higher Ci value compared with those of OFF tree leaves. The lower Pn values confirm that photosynthesis activities are lower in ON tree leaves than in OFF tree leaves. Energy Processes Carbohydrate metabolism is important for energy production and conversion. Most of the identified DEPs were associated with carbohydrate biosynthesis, metabolism, and transport. Processes related to energy included glycolysis, gluconeogenesis, and the pentose phosphate and tricarboxylic acid (TCA) pathways. Nutrient imbalances influence the AB phenomenon,8-9 and the balance between sources and sinks is also important for the growth and development of individual fruit trees. The AB phenotype is associated with energy production and conversion processes that inhibit normal growth. Among the DEPs, we identified glucose-6-phosphate (MDP0000139910), β-fructofuranosidase (MDP0000561738), 25



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β-glucanase (MDP0000158544), glucose-1-phosphate adenylyltransferase (MDP0000258518), and β-1,3-glucanse (new apple gene 2492), which are involved in carbohydrate metabolism and glycolysis. The levels of the hydrolytic enzyme glucose-6-phosphate were higher in ON tree leaves than in OFF tree leaves, which was consistent with the observed lower carbohydrate contents in ON tree samples (Figure 4). The abundance of fructofuranosidase (MDP0000561738), which is a sucrose invertase, was also higher in ON tree leaves. Increased fruit production in ON trees results in lower available energy sources for flower bud development in the following spring. In contrast, with fewer blossoming flowers, OFF trees can accumulate more sources of energy and nutrition to enhance flower induction in the subsequent year. Glycolysis is an important process for energy production as it can oxidize hexoses to generate ATP. 72We detected several proteins involved in glycolysis, suggesting glycolysis affected the AB mechanisms. The TCA cycle is a major pathway affecting carbohydrates, lipids, and amino acids. Of the identified proteins associated with the TCA cycle, the abundance of aconitate hydratase (MDP0000279281), two ATP synthases (MDP0000291815 and new apple gene 7470), and an ATP-dependent zinc protease (MDP0000289910) was lower in ON tree leaves than in OFF tree leaves. In contrast, the ADP/ATP carrier protein (MDP0000222589) levels were higher in ON tree leaves. The ATP synthases have diverse functions that regulate plant growth, sucrose translocation, stomatal pore opening, and redox regulation.73 The OFF tree leaves are believed to maintain high ATP synthase levels to enhance plant growth and conserve energy. The observed 26


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differences between the ON and OFF trees regarding energy processes suggest the TCA cycle influences the AB phenomenon. Overall, the complex carbohydrate metabolic activities and glycolysis may simultaneously regulate the AB phenotype.

Metabolic Processes A broad range of proteins related to the metabolism of amino acids, nucleotides, phosphates, lipids, and sterols was also detected (Table 3). The production of proteins involved in flavonol biosynthesis and isoflavonoid pathways was generally induced. Caffeic acid 3-O-methyltransferase (MDP0000656929) and chalcone synthase (MDP0000126567), which are involved in flavonoid biosynthesis, were more abundant in ON tree leaves than in OFF tree leaves. Flavonoids affect several fruit characteristics, including aroma, flavor, pigmentation and anthocyanin production. 74 Flavonoids are synthesized under conditions of excess photoassimilates, particularly sucrose. 74 Proteins involved in flavonoid production are affected by photoassimilates and other carbon molecules. Aminomethyl transferase (MDP0000805894), which metabolizes nitrogen, was significantly less abundant in ON tree leaves than in OFF tree leaves. This was consistent with the observed nitrogen levels (Figure 4).

Other Biological Processes Proteins related to defense, cell structure, inorganic ion transport, and protein synthesis and turnover were all differentially expressed between the ON and OFF tree leaves (Table 3; Table S3 and S4). However, these processes involved fewer proteins 27



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than those associated with energy and metabolism. The proteins affecting defense/disease processes were heat-shock and pathogenesis-related proteins (Table 2). Peroxidases (MDP0000272643 and MDP0000272643) were more abundant in the ON tree leaves than in the OFF tree leaves, while catalases (MDP0000309331 and MDP0000132452) exhibited the opposite pattern. These results are consistent with the idea that redox activities remain high during fruit growth. 75Our findings are similar to those observed in mango leaves. 7. Proteins with unknown functions may also regulate the AB phenotype.

Overview of the Primary Metabolic Pathways Influencing the AB Phenomenon Our whole iTRAQ protein profile identified more DEPs and confirmed detailed biological processes including photosynthesis, carbohydrate metabolism, glycolysis, protein processing, redox activities and secondary metabolism in apple. This work established the whole post-transcriptional modifications and cellular functions of proteins in AB. Additionally, our proteomics study has revealed that complex networks affect the AB phenotype. We have also discussed the potential roles of several proteins involved in multiple biological processes. A hypothetical model for the regulation of the AB phenotype has been developed based on the current results (Figure 11). Fruit load affects the flowering induction and produces an AB signal. These three factors may explain how the AB phenomenon is regulated in Malus species and other perennial woody plants. First, photosynthesis-related factors, such as the thylakoid membrane phosphoprotein, photosystem reaction center, and 28


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chlorophyll-binding protein, are believed to affect AB. Proteins associated with carbohydrate metabolism and glycolysis are also important regulatory factors. We detected various enzymes involved in carbohydrate synthesis, transport, and hydrolysis, as well as glycolysis. These proteins may affect cell division and plant growth, and also participate in sugar transport and sugar signaling-related transcriptional signatures. Finally, although few hormone-related DEPs were identified in this study, hormones such as GA, ZR, IAA, and brassinosteroids is indeed involved in regulating the AB phenomenon. Several genes affecting hormone responses and signaling can influence the expression of flowering controlling genes. These endogenous hormones also mediate plant growth. These factors together with other genes controlling flowering are involved in regulating the AB phenotype.

CONCLUSIONS This proteome-level examination of the complex AB mechanisms of the apple tree has generated potentially valuable molecular data. We used iTRAQ technology to evaluate DEPs in the leaves of ON and OFF trees. We hope to enable analysis of global protein expression patterns influencing critical biological processes. Several DEPs affecting critical biological processes were identified as being associated with the AB phenotype. Our results indicate that photosynthesis, carbohydrate metabolism, protein processing, and secondary metabolism have important roles and further 29



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investigations showed photosynthesis was the most remarkable process. Additionally, hormones and some flowering control genes also mediate the AB phenomenon. The data presented herein may help to characterize the processes and proteins regulating AB in apple trees. However, there are still unanswered questions. The AB phenotype has considerable economic consequences for many perennial woody plants. We cannot figure out one single factor for the sophisticated AB. The most remarkable photosynthesis as well as other processes led to be better understanding of AB. Our results may form the basis for future studies focused on AB fruit trees. We hope our findings can enlighten researchers on this complex mechanism of AB. The continued work should be focused on investigating on photosynthesis and other valuable processes according to our findings.

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENTS This work was ﬁnancially supported by the National Science and Technology Supporting Project (2013BAD20B03), China Apple Research System (CARS-28), National Spark Plan Program (2014GA850002), Science and Technology Innovative Engineering Project in Shaanxi province, China (2015NY114), China Postdoctoral Science Foundation (2014M56806), National Natural Science Foundation of China 30


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(31672101), Yangling Subsidiary Center Project of the National Apple Improvement Center and Collaborative Innovation of the Center for Shaanxi Fruit Industry Development.

ABBREVIATIONS AB, alternate bearing;iTRAQ, isobaric tags for relative and absolute quantitation; DAFB, days after full blossom; DEPs, differentially expressed proteins; COG, Cluster of Orthologous Groups of proteins; KEGG, Kyoto Encyclopedia of Genes and Genomes

ASSOCIATED CONTENT Supporting Information Figure S1. The repeatability of quantification data between two biological replicates. Figure S2. Abundance distribution of differentially expressed proteins in the leaves of ON and OFF trees based on two biological replicates. Figure S3. Functional classification of identified proteins from the leaves of ON and OFF trees. Figure S4. Detailed models of the important biological processes involved in AB. The accession numbers of DEPs were attached. Red, up-regulated; Green, down-regulated. Table S1. Primers used for quantitative PCR. Table S2. All proteins identified in this study. Table S3. Up-regulated proteins in ON tree leaves relative to the corresponding abundance in OFF tree leaves. Table S4. Down-regulated proteins in ON tree leaves relative to the corresponding abundance in OFF tree leaves. 31



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REFERENCES (1) Shalom, L.; Samuels, S.; Zur, N.; Shlizerman, L.; Zemach, H.; Weissberg, M.; Ophir, R.; Blumwald, E.; Sadka, A. Alternate Bearing in Citrus: Changes in the Expression of Flowering Control Genes and in Global Gene Expression in ON- versus OFF-Crop Trees. Plos One 2012, 7(10), e46930. (2) Verreynne, J. S.; Lovatt, C. J. The Effect of Crop Load on Budbreak Influences Return Bloom in Alternate Bearing 'Pixie' Mandarin. J. Am. Soc. Hort. Sci. 2009, 134, 299–307. (3) Singh, L. Studies on biennial bearing. III. Growth studies on ‘on’ and ‘off’ year trees. J. Hort. Sci. 1948, 24, 123–148. (4) Jonkers, H. Biennial bearing in apple and pear: a literature survey. Sci. Hort. 1979, 11, 303–307. (5) Monselise, S.; Goldschmidt E. Alternate bearing in fruit trees. Hort. Reviews. 1982,4, 128–173. (6) Bangerth, F. Flower induction in perennial fruit trees: still an enigma? Acta Hort. 2006, 727, 177–195. (7) Munoz-Fambuena, N.; Mesejo, C.; Agusti, M.; Tarraga, S.; Iglesias, D. J.; Primo-Millo, E.; Gonzalez-Mas, M. C. Proteomic analysis of "Moncada" mandarin leaves with contrasting fruit load. Plant Physiol. Biochem. 2013, 62, 95–106. (8) Goldschmidt, EE; Aschkenazi N; Herzano Y; Schaffer AA; Monselise SP. A role for carbohydrate-levels in the control of flowering in citrus. Sci. Hort.1985, 26, 159–166. (9) Goldschmidt, EE. Carbohydrate supply as a critical factor for citrus fruit development and productivity. HortScience. 1999, 34, 1020–1024. (10) Monselise, S.; Goldschmidt E. Alternate bearing in citrus and ways of control. Proc. Inter. Soc. Citr. 1981, 1239–1242. (11) Goldberg-Moeller, R.; Shalom, L.; Shlizerman, L.; Samuels, S.; Zur, N.; Ophir, R.; Blumwald, E.; Sadka, A. Effects of gibberellin treatment during flowering induction period on global gene expression and the transcription of flowering-control genes in Citrus buds. Plant Sci 2013, 198, 46–57. (12) Shalom, L.; Samuels, S.; Zur, N.; Shlizerman, L.; Doron-Faigenboim, A.; Blumwald, E.; Sadka, A. Fruit load induces changes in global gene expression and in abscisic acid (ABA) and indole acetic acid (IAA) homeostasis in citrus buds. J. Exp. Bot .2014, 65, 3029–3044. (13) Li, Y. ; Zhang, D; Xing, L.; Zhang, S.; Zhao, C.; Han, M. Effect of exogenous 6-benzylaminopurine (6-BA) on branch type, floral induction and initiation, and related gene expression in ‘Fuji’apple (Malus domestica Borkh). Plant Growth Regul .2015,1–6 (14) Koshita, Y.; Takahara, T.; Ogata, T.; Goto, A. Involvement of endogenous plant hormones (IAA, ABA, GAs) in leaves and flower bud formation of satsuma mandarin (Citrus unshiu Marc.). Sci. Hort. 1999, 79, 185–194. (15) Okuda, H. A comparison of IAA and ABA levels in leaves and roots of two citrus cultivars with different degrees of alternate bearing. J. Hort. Sci.&Bio. 2000,75, 33



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

355–359. (16) Caaejas, R.; Bangerth, F. Is auxin export of apple fruit an alternate signal for inhibition of flowering bud induction. Acta Hort. 1997,463,271–277. (17) Ziv, D.; Zviran, T.; Zezak, O.; Samach, A.; Irihimovitch, V. Expression profiling of FLOWERING LOCUS T-like gene in alternate bearing 'Hass' avocado trees suggests a role for PaFT in avocado flower induction. Plos One 2014, 9 (10), e110613. (18) Chan, G., Cain, J. The effect of seed formation onsubsequent flowering in apple. Pro. Amer. Soc. Hort. Sci. 1967,91, 63–68. (19) Neilsen, J., Dennis, F. Effects of seed number, fruit removal, bourse shoot length and crop density on flowering in ‘Spenser Seedless’ apple. Acta Hort.2000,527, 137–146. (20) Xing, L.; Zhang, D.; Zhao, C.; Li, Y.; Ma, J.; An, N.; Han, M. Shoot bending promotes flower bud formation by miRNA-mediated regulation in apple (Malus domestica Borkh.). Plant Biotech. J. 2016, 14 (2), 749–770. (21) Bai, S; Saito, T.; Ito, A.; Tuan, P. A., Xu, Y., Teng, Y. W., Morguchi, T. Small RNA and PARE sequencing in flower bud reveal the involvement of sRNAs in endodormancy release of Japanese pear (Pyrus pyrifolia'Kosui') BMC genomics, 2016, 17(1), 1. (22) Niu, Q; Li, J.; Cai, D.; Qian, M.; Jia, H.; Bai, S.; Hussain, S.; Liu, G.; Teng, Y.; Zheng, X. Dormancy-associated MADS-box genes and microRNAs jointly control dormancy transition in pear (Pyrus pyrifolia white pear group) flower bud. J. Exp. Bot. 2016, 67, (1), 239–257. (23) Zhu H, Xia R, Zhao B: An Y-q, Dardick CD, Callahan AM, Liu Z. Unique expression, processing regulation, and regulatory network of peach (Prunus persica) miRNAs. BMC Plant Biol.2012, 12(1). (24) Kotoda, N.; Hayashi, H.; Suzuki, M.; Igarashi, M.; Hatsuyama, Y.; Kidou, S.; Igasaki, T.; Nishiguchi, M.; Yano, K.; Shimizu, T.; Takahashi, S.; Iwanami, H.; Moriya, S.; Abe, K. Molecular Characterization of FLOWERING LOCUS T-Like Genes of Apple (Malusdomestica Borkh.). Plant Cell Physiol. 2010, 51, (4), 561–575. (25) Zhao, Q.; Sun, C.; Liu, D. D.; Hao, Y. J.; You, C. X. Ectopic expression of the apple Md-miR172e gene alters flowering time and floral organ identity in Arabidopsis. Plant Cell Tissue and Organ Culture 2015, 123, (3), 535–546. (26) Sun, C.; Zhao, Q.; Liu, D.; You, C.; Hao, Y. Ectopic expression of the apple Md-miRNA156h gene regulates flower and fruit development in Arabidopsis. Plant Cell Tissue and Organ Culture 2013, 112, (3), 343–351. (27) Flachowsky, H.; Szankowski, I.; Waidmann, S.; Peil, A.; Trankner, C.; Hanke, M. V. The MdTFL1 gene of apple (Malus×domestica Borkh.) reduces vegetative growth and generation time. Tree Phy. 2012, 32, (10), 1288–1301. (28) Nakagawa, M.;Honsho C.; Kanzaki, S.; Shimizu, K.; Utsunomiya, N. Isolation and expression analysis of FLOWERING LOCUS T-like andgibberellin metabolism genes in biennial-bearing mango trees. Sci.Hort. 2012,139, 108–117. (29) Evans, J. Photosynthesis and nitrogen relationships in leaves of C-3 plants. Oecologia 1989 78, 9–19. 34


Page 34 of 57

Page 35 of 57

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(30) Monerri, C; Fortunato-Almeida, A; Molina, R.; Nebauer, S.; Garcia-Luis, A.; Guardiola, J. Relation of carbohydrate reserves with the forthcoming crop, flower formation and photosynthetic rate, in the alternate bearing ’ Salustiana’ sweet orange (Citrus sinensis L.). Sci. Hort. 2011, 129, 71–78. (31) Li, C.; Weiss, D.; Goldschmidt, EE. Girdling affects carbohydrate related gene expression in leaves, bark and roots of alternate-bearing citrus trees. Annals of Botany 2003b, 92, 137–143. (32) Ralhan, R.; Desouza, L. V.; Matta, A.; Chandra Tripathi, S.; Ghanny, S.; Datta Gupta, S.; Bahadur, S.; Siu, K. W. Discovery and verification of head-and-neck cancer biomarkers by differential protein expression analysis using iTRAQ labeling, multidimensional liquid chromatography, and tandem mass spectrometry. Mol. Cell. Proteomics MCP 2008, 7 (6), 1162–1173. (33) Ross P.; Huang,Y.;Marchese, J.;Williamson, B.; Parker, K.; Hattan, S. Multiplexed protein quantitation in Saccharomycescerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell Proteomics MCP 2004, 3, 1154–1169. (34) Bindschedler, L.V.; Cramer, R. Quantitative plant proteomics. Proteomics 2011, 11, 756–775. (35) Lee,J.; Cao, L.; Ow, S.; Barrios-Llerena, M.; Chen, W.; Wood, T. Proteome changes after metabolic engineering to enhance aerobic mineralization of cis-1,2-dichloroethylene. J. Proteome Res. 2006, 5, 1388–1397. (36) Zhou, S.; Li, M.; Guan, Q.; Liu, F.; Zhang, S.; Chen, W.; Yin, L.; Qin, Y.; Ma, F. Physiological and proteome analysis suggest critical roles for the photosynthetic system for high water-use efficiency under drought stress in Malus. Plant Sci 2015, 236, 44–60. (37) Wu, H. X.; Jia, H. M.; Ma, X. W.; Wang, S. B.; Yao, Q. S.; Xu, W. T.; Zhou, Y. G.; Gao, Z. S.; Zhan, R. L. Transcriptome and proteomic analysis of mango (Mangifera indica Linn) fruits. J. Proteomics 2014, 105, 19–30. (38) P, R. N.; Campos-Vargas, R.; Orellana, A. Assessment of Prunus persica fruit softening using a proteomics approach. J. Proteomics 2012, 75 (5), 1618–38. (39) Liu, X.; Zhai, R.; Feng, W.; Zhang, S.; Wang, Z.; Qiu, Z.; Zhang, J.; Ma, F.; Xu, L. Proteomic analysis of 'Zaosu' pear (Pyrus bretschneideri Rehd.) and its early-maturing bud sport. Plant Sci 2014, 224, 120–135. (40) Wang, Z. Q.; Xu, X. Y.; Gong, Q. Q.; Xie, C.; Fan, W.; Yang, J. L.; Lin, Q. S.; Zheng, S. J. Root proteome of rice studied by iTRAQ provides integrated insight into aluminum stress tolerance mechanisms in plants. J. Proteomics 2014, 98, 189–205. (41) Gil-Quintana, E.; Lyon, D.; Staudinger, C.; Wienkoop, S.; Gonzalez, E. M.Medicago truncatula and Glycine max: Different Drought Tolerance and Similar Local Response of the Root Nodule Proteome. J.Proteome Res. 2015, 14, (12), 5240–5251. (42) He, D. L.; Wang, Q.; Li, M.; Damaris, R. N.; Yi, X. L.; Cheng, Z. Y.; Yang, P. F.Global Proteome Analyses of Lysine Acetylation and Succinylation Reveal the Widespread Involvement of both Modification in Metabolism in the Embryo of Germinating Rice Seed. J.Proteome Res. 2016, 15, (3), 879–890. (43) Zhang Q.; Han M.; Song C.; Song X.; Zhao C.; Liu H.; Hirst P.M.;Zhang D. 35



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Optimizing planting density for production of high-quality apple nursery stock in China. New Zealand J. Cro. Hort. Sci. 2015, 43(1): 7–17. (44) Kacar, B. Chemical Analyses of Plant and Soil. 1972; pp. 646. (45) Jordan ,M. O.; Vercambre, G.; Bot, J.L.; Adamowicz, S.;Gomez, L.; Pages, L. Autumnal nitrogen nutrition affects the C and N storage and architecture of young peach trees. Trees: Structure and Function 2011,25,333–344. (46) Cheng, L.; Fuchigami, L. H. Growth of young apple trees in relation to reserve nitrogen and carbohydrates. Tree physiol. 2002, 22 (18), 1297–1303. (47) Rosa, M.; Hilal, M.; Gonzalez, J. A.; Prado, F. E. Low-temperature effect on enzyme activities involved in sucrose-starch partitioning in salt-stressed and salt-acclimated cotyledons of quinoa (Chenopodium quinoa Willd.) seedlings. Plant Physiol. Biochem. 2009, 47 (4), 300–307. (48) Weiler, E. W.; Jourdan, P. S.; Conrad, W. Levels of indole-3-acetic acid in intact and decapitated coleoptiles as determined by a specific and highly sensitive solid-phase enzyme immunoassay. Planta 1981, 153 (6), 561–571 (49) Du, C.; Liang, J. R.; Chen, D. D.; Xu, B.; Zhuo, W. H.; Gao, Y. H.; Chen, C. P.; Bowler, C.; Zhang, W. ITRAQ-based proteomic analysis of the metabolism mechanism associated with silicon response in the marine diatom Thalassiosira pseudonana. J.Proteome Res. 2014, 13 (2), 720–734. (50) Pan, X.; Zhu, B.; Zhu, H.; Chen, Y.; Tian, H.; Luo, Y.; Fu, D. ITRAQ protein profile analysis of tomato green-ripe mutant reveals new aspects critical for fruit ripening. J.Proteome Res. 2014, 13 (4), 1979–1993. (51) Zhu, M.; Dai, S.; McClung, S.; Yan, X.; Chen, S. Functional differentiation of Brassica napus guard cells and mesophyll cells revealed by comparative proteomics. Mol. Cell Proteomics MCP 2009, 8 (4), 752–766. (52)Elias, J. E., and Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods, 2007, 4, 207–214 (53) Xing, L. B.; Zhang, D.; Li, Y. M.; Shen, Y. W.; Zhao, C. P.; Ma, J. J.; An, N.; Han, M. Y. Transcription Profiles Reveal Sugar and Hormone Signaling Pathways Mediating Flower Induction in Apple (Malus domestica Borkh.). Plant & cell Physiol. 2015, 56 (10), 2052–2068. (54) Gambino, G.; Perrone, I.; Gribaudo, I. A Rapid and effective method for RNA extraction from different tissues of grapevine and other woody plants. Phytochem. Anal. 2008, 19, (6), 520–525. (55) Porri, A.; Torti, S.; Romera-Branchat, M.; Coupland, G. Spatially distinct regulatory roles for gibberellins in the promotion of flowering of Arabidopsis under long photoperiods. Development 2012, 139, (12), 2198–2209. (56)Galvao, V. C.; Horrer, D.; Kuttner, F.; Schmid, M. Spatial control of flowering by DELLA proteins in Arabidopsis thaliana. Development 2012, 139, (21), 4072–82. (57) Krapp, A. Plant nitrogen assimilation and its regulation: a complex puzzle with missing pieces. Curr. Opin. Plant Biol. 2015, 25, 115–22. (58) Lambers, H.; Martinoia, E.; Renton, M. Plant adaptations to severely phosphorus-impoverished soils. Curr .Opin. Plant. Biol .2015, 25, 23–31. 36


Page 36 of 57

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(59) Bernier, G.; Perilleux, C. A physiological overview of the genetics of flowering time control. Plant Biotech. J. 2005, 3, (1), 3–16. (60) Hisamatsu, T.; King, R. W. The nature of floral signals in Arabidopsis. II. Roles for FLOWERING LOCUS T (FT) and gibberellin. J. Exp. Bot.2008, 59, (14), 3821–3829. (61) Eshghi, S.; Tafazoli, E. Possible role of non-structural carbohydrates in flower induction in strawberry. J. Hort.Sci.Biotech. 2006, 81, (5), 854–858. (62) Goldschmidt, EE.; Golomb, A. The carbohydrate balance of alternate-bearing citrus trees and the significance of reserves for flowering and fruiting. J. Am. Soc. Hort. Sci. 1982, 107, 206–208. (63) Wilkie, J. D.; Sedgley, M.; Olesen, T. Regulation of floral initiation in horticultural trees. J. Exp. Bot. 2008, 59, (12), 3215–3228. (64) Ulger, S.; Sonmez, S.; Karkacier, M.; Ertoy, N.; Akdesir, O.; Aksu, M. Determination of endogenous hormones, sugars and mineral nutrition levels during the induction, initiation and differentiation stage and their effects on flower formation in olive. Plant Growth Regul. 2004, 42, 89–95. (65) Boss, P. K.; Thomas, M. R. Association of dwarfism and floral induction with a grape 'green revolution' mutation. Nature 2002, 416, (6883), 847–850. (66) Jung, J. H.; Ju, Y.; Seo, P. J.; Lee, J. H.; Park, C. M. The SOC1-SPL module integrates photoperiod and gibberellic acid signals to control flowering time in Arabidopsis. Plant J. 2012, 69 (4), 577–588. (67) Mimida, N.; Kidou, S.; Iwanami, H.; Moriya, S.; Abe, K.; Voogd, C.; Varkonyi-Gasic, E.; Kotoda, N. Apple FLOWERING LOCUS T proteins interact with transcription factors implicated in cell growth and organ development. Tree Physiol. 2011, 31, (5), 555–566. (68) Wientjes, E.; van Amerongen, H.; Croce, R. LHCII is an antenna of both photosystems after long-term acclimation. Biochim. Biophys. Acta. 2013, 1827, (3), 420–426. (69) van Amerongen, H.; Croce, R. Light harvesting in photosystem II. Photosynth Res. 2013, 116, (2-3), 251–63. (70) Labate, M. T.; Ko, K.; Ko, Z. W.; Pinto, L. S.; Real, M. J.; Romano, M. R.; Barja, P. R.; Granell, A.; Friso, G.; van Wijk, K. J.; Brugnoli, E.; Labate, C. A. Constitutive expression of pea Lhcb 1-2 in tobacco affects plant development, morphology and photosynthetic capacity. Plant Mol. Biol. 2004, 55, (5), 701–714. (71) Millar, A. J.; Kay, S. A. Integration of circadian and phototransduction pathways in the network controlling CAB gene transcription in Arabidopsis. Proc. Natl. Acad. Sci . 1996, 93, (26), 15491–15496. (72) Plaxton, W. C. The Organization and Regulation of Plant Glycolysis. Annu Rev. Plant Physiol. 1996, 47, 185–214. (73) Gevaudant, F.; Duby, G.; von Stedingk, E.; Zhao, R.; Morsomme, P.; Boutry, M. Expression of a constitutively activated plasma membrane H+-ATPase alters plant development and increases salt tolerance. Plant Physiol. 2007, 144, (4), 1763–1776. (74) Grace, M. H.; Wilsonb, G. R.; Kandil, F. E.; Dimitriadis, E.; Coates, R. M. Characteristic flavonoids from Acacia burkittii and A. acuminata heartwoods and their 37



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differential cytotoxicity to normal and leukemia cells. Nat. Prod. Commun. 2009, 4, (1), 69–76. (75) Abassi N A; Kushad M M; Endress A G.Active oxygen-scavenging enzymes activities in developing apple flowers and fruits. Sci. Hort.1998, 74(3): 183–194.

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FIGURE LEGENDS

Figure 1. Morphology of the alternate bearing Malus prunifolia tree. (free domain) Leaves from the ON (A) and OFF (B) trees. The alternate bearing phenotype in 2014 (C) and 2015 (D).

Figure 2. Workflow for the analysis of protein expression in the leaves of ON and OFF trees involving iTRAQ labeling. T1 and T2: leaves from the ON tree. T3 and T4: leaves from the OFF tree.

Figure 3. Temporal changes in leaf morphology indices for ON and OFF trees. Fresh (A) and dry (B) weight per 100 leaves. (C) Average leaf areas. Values are presented as the mean ± standard error of three replicates.

Figure 4. Abundance of minerals and sugars in the leaves of ON and OFF trees. (A) Nitrogen content; (B) Phosphorus content; (C) Potassium content; (D) Sucrose content; (E) Glucose content; (F) Fructose content; (G) Sorbitol content; (H) Total sugar content; (I) Starch content.

Figure 5. Endogenous hormone levels in the leaves of ON and OFF trees. (A) Zeatin riboside content; (B) Auxin content; (C) Abscisic acid content; 39



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(D) Gibberellin content (GA1+3); (E) Gibberellin content (GA4+7); (F) Zeatin riboside:gibberellin ratio (gibberellins include GA1+3 and GA4+7)

Figure 6. Results of the iTRAQ liquid chromatography/tandem mass spectrometry analysis of leaves from ON and OFF trees. (A) Identified Malus prunifolia proteins. (B) Distribution of proteins with different molecular weights. (C) Protein coverage by the identified peptides.

Figure 7. Diagrams of common proteins in ON and OFF tree leaves according to Gene Ontology annotations. Number and proportions of proteins in the three functional classification categories (i.e., biological processes, molecular functions, or cellular components) are presented.

Figure 8. Protein levels of the candidate photosynthesis proteins in ON and OFF tree leaves. Proteins at 30, 45 and 60 DAFB in ON and OFF tree leaves were used for immunoblotting analysis. GAPDH was used as a control.

Figure 9. Relative expression levels of differentially expressed proteins in ON and OFF tree leaves.

Figure 10. Relative expression levels of genes controlling flowering in in ON and OFF tree leaves. 40


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Figure 11. Hypothetical model of the critical metabolic processes affecting the alternate bearing Malus prunifolia tree. Metabolic steps are represented by arrows. Numbers in ellipses represent the number of differentially expressed proteins. The up-regulated and down-regulated proteins are indicated with red and green arrows, respectively. Numbers in the top left part of arrows correspond to the number of proteins. And the detailed regulatory changes of the important biological processes can be seen in supplement figure 4.

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Tables Table 1 Photosynthetic parameters in leaves of ON tree and OFF tree Date 45DAF 90 DAF

Type ON tree OFF tree ON tree OFF tree

Pn 9.21±0.19 12.68±0.32 10.36±0.28 13.15±0.57

Gs 221.18±8.25 216.91±9.19 229.15±7.62 267.34±6.03

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Ci 317.91±6.36 309.27±5.23 310.8±9.27 325.87±7.29

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Table 2. KEGG pathway enrichment analysis of DEPs (P < 0.05) DEPs (505)a

All proteins (4050)b

Pathway ID

p-Valuec

Photosynthesis

30 (5.94%)

57（1.41%）

Ko00195

1.826329e-13

Photosynthesis- antenna proteins

17 (3.37%)

20 (0.49%)

Ko00196

2.656999e-13

Glyoxylate and dicarboxylate metabolism

19 (3.76%)

57 (1.41%)

Ko00630

3.109354e-05

Metabolic pathways

202 (40%)

1363 (33.65%)

ko01101

0.0008367697

Fructose and mannose metabolism

17 (3.37%)

68 (1.68%)

Ko00051

0.003235905

Carbon fixation in photosynthetic

19 (3.76%)

81 (2%)

Ko00710

0.004125975

Protein processing in endoplasmic

28 (5.54%)

141（3.48%）

Ko04141

0.00736331

Pentose phosphate pathway

12 (2.38%)

46 (1.41%)

Ko00030

0.00882276

117（23.17%）

794（19.16%）

ko01110

0.01932900

13 (2.57%)

57 (1.41%)

Ko00260

0.02051753

5(0.99%)

15(0.37%)

Ko00943

0.03050939

24 (4.57%)

131 (3.23%)

Ko00500

0.03166480

Pathway

Biosynthesis of secondary metabolites Glycine, serine and threonine metabolism Isoflavonoid biosynthesis Starch and sucrose metabolism a

DEPs were analyzed with pathway annotation.

b

ALL proteins were analyzed with pathway annotation.

c

Pathways with p-value higher than 0.05 were not listed.

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Table 3. List of modulated proteins in alternative bearing apple tree. Accession numbera Energy MDP0000212157 MDP0000158544 MDP0000218699 MDP0000246775 Apple_newGene2492 MDP0000139910 MDP0000561738 MDP0000948729 MDP0000222589 MDP0000279281 MDP0000291815 MDP0000153663 MDP0000838506 MDP0000757636 MDP0000419318 MDP0000217142 MDP0000132802 MDP0000337585 MDP0000150729 MDP0000880187 Apple_newGene7470 MDP0000601491 MDP0000210616 MDP0000258518 MDP0000319969 MDP0000293270 MDP0000269859 MDP0000142895 MDP0000909197 MDP0000252563 Metabolism MDP0000427722 MDP0000126567 MDP0000804949 MDP0000209662 MDP0000210966 MDP0000656929 MDP0000316281 MDP0000679173

Name

COV (95)%b

Peptid (95)%c

iTRAQ Ratio (ON/OFF) ± SD

Isocitrate dehydrogenases Beta-glucanase Thaumatin-like protein Thaumatin-like protein Beta-1,3-glucanase Glucose-6-phosphate Beta-fructofuranosidase UPF0481 protein ADP,ATP carrier protein Aconitate hydratase ATP synthase Chlorophyll a-b binding protein P4 Chloroplast stem-loop binding protein Chlorophyll a-b binding protein CP29 Oxygen-evolving enhancer protein Chloroplastic protein Photosystem II reaction center psb28 protein Chlorophyll a-b binding protein 7 Chlorophyll a-b binding protein 8 Chloroplastic protein ATP synthase Chlorophyll a-b binding protein Thylakoid membrane phosphoprotein Glucose-1-phosphate adenylyltransferase Cytochrome b6f Rieske iron Quinone oxidoreductase Chlorophyll a-b binding protein 8 Photosystem I reaction center subunit VI Photosystem I reaction center subunit VI Quinone oxidoreductase

23.9 16 5.6 5.6 57.3 3 18.8 2.6 26.9 29.4 8.7 51 44.1 27.6 41 23.4 8.9 26.9 37.9 36.5 22 61.5 4.1 45.8 23.8 43.8 32.4 26.4 47.9 28.7

11 4 4 2 10 3 13 1 13 21 3 6 13 6 8 3 1 5 10 15 1 12 1 23 4 18 8 4 4 12

2.998±0.359 2.198±0.091 1.870±0.200 1.857±0.181 1.741±0.018 1.722±0.142 1.711±0.069 1.671±0.177 1.642±0.541 0.654± 0.097 0.647±0.056 0.645±0.219 0.639±0.144 0.631± 0.115 0.614±0.003 0.612± 0.119 0.601±0.022 0.596±0.020 0.596±0.024 0.592±0.041 0.586± 0.118 0.563±0.011 0.552±0.204 0.535±0.129 0.506±0.006 0.470± 0.128 0.465± 0.229 0.446± 0.025 0.401±0.021 0.319±0.019

MLP-like protein Chalcone synthase Cinnamyl alcohol dehydrogenase Desiccation-related protein Choline dehydrogenase Caffeic acid 3-O-methyltransferase Serine hydroxymethyltransferase S-adenosylhomocysteine hydrolase

39.2 9.8 23.9 25.3 9.5 35.3 33.7 29.9

6 4 7 5 2 10 11 11

2.123±0.034 2.098±0.019 2.076±0.039 2.043±0.047 1.904±0.151 1.762±0.197 1.683±0.260 1.671±0.123

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MDP0000208208 Threonine-protein MDP0000313843 Ureide permease MDP0000123549 G3BP-like protein MDP0000156866 Alcohol dehydrogenase MDP0000235454 Acetyl-coa acetyltransferase MDP0000805894 Aminomethyl transferase MDP0000508187 Serine--glyoxylate aminotransferase MDP0000605612 Apocytochrome Apple_newGene528 5'-adenylylsulfate reductase Disease/defense MDP0000272643 Peroxidase Apple_newGene6008 Pathogenesis-related protein MDP0000295542 Major allergen protein Apple_newGene73 Peroxidase MDP0000811918 Ferredoxin MDP0000759437 Ferredoxin MDP0000309331 Catalase MDP0000132452 Catalase Cell structure MDP0000192236 Histone H2A MDP0000817733 Epidermis-specific secreted glycoprotein MDP0000219700 Periplasmic protease MDP0000321498 Nucleoside-diphosphate MDP0000371499 Nucleoside-diphosphate Protein synthesis and turn over MDP0000298783 20S proteasome MDP0000128876 Ribosomal protein MDP0000201265 Leucine-rich repeat (LRR) protein MDP0000642077 Glutaredoxin MDP0000703640 Prolyl cis-trans isomerase MDP0000272177 Subtilisin-like serine proteases Function unknown MDP0000207294 Uncharacterized protein MDP0000208850 Uncharacterized protein MDP0000136132 Uncharacterized protein MDP0000092987 Uncharacterized protein MDP0000121380 Uncharacterized protein a

2 11.8 4 21 23.6 40.4 52.9 35 16.7

2 9 2 8 8 14 14 9 4

1.643±0.281 1.571±0.164 1.553±0.016 1.548±0.440 1.531±0.079 0.663±0.117 0.657±0.018 0.592±0.012 0.467± 0.052

17.6 8.1 70.4 2.8 39.4 36.6 17.1 50.6

4 1 9 1 13 7 11 15

1.665±0.112 1.633±0.424 1.597±0.231 1.560±0.217 0.665±0.007 0.626±0.115 0.591±0.077 0.588± 0.017

26 19.8 9 4.2 22.6

3 7 5 4 8

1.760±0.423 1.563±0.200 0.658±0.022 0.645±0.060 0.637± 0.080

14.1 15.2 11 58.9 24.1 33.8

5 2 5 4 5 10

2.298±0.229 1.816±0.781 1.691±0.079 1.650±0.625 0.621±0.064 0.529±0.013

13.2 14.5 16 10.8 12

8 3 3 2 2

1.569±0.445 0.656± 0.079 0.593±0.058 0.540± 0.174 0.481±0.128

Accession no. is the locus name of a gene in apple Genome.

b

% Cov (95) indicates the percentage of matching amino acids from identified peptides having confidence greater than or equal to 95%.

c

Peptides (95%) indicate the number of distinct peptides having at least 95% confidence.

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Figure 1.

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Figure 2.

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Figure 3.

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Figure 4.

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Figure 5.

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Figure 6.

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Figure 7.

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Figure 8.

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Figure 9.

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Figure 10.

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Figure 11.

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TOC graphic

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Proteome Analyses Using iTRAQ Labeling Reveal ... - ACS Publications

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