ITRAQ-Based Proteomic Analysis of the Metabolic Mechanisms

Sep 27, 2016 - ITRAQ-Based Proteomic Analysis of the Metabolic Mechanisms Behind Lipid Accumulation and Degradation during Peanut Seed Development and...
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ITRAQ-based proteomic analysis of the metabolic mechanisms behind lipid accumulation and degradation during peanut seed development and post germination Yun Wang, Xingli Ma, Xingguo Zhang, Xiaoyan He, Hemin Li, Dangqun Cui, and Dongmei Yin J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00345 • Publication Date (Web): 27 Sep 2016 Downloaded from http://pubs.acs.org on September 27, 2016

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ITRAQ-based proteomic analysis of the metabolic mechanisms behind lipid accumulation and degradation during peanut seed development and post germination Yun Wang, Xingli Ma, Xingguo Zhang, Xiaoyan He , Hemin Li，Dangqun Cui，Dongmei Yin*, Henan Agricultural University, Zhengzhou 450002, China *

Corresponding author: Email: [email protected] , [email protected]

Abstract Peanut seeds have a high oil content making it an important oil crop. During development and germination, seeds undergo complex dynamic and physiological changes. Changes in lipid metabolism and underlying mechanisms during seed development have been studied extensively by DNA and RNA sequencing; however, there are few studies on dynamic changes of proteomics during peanut seed development and germination. In this study, proteomic analyses were carried out 20, 40, 60 and 80 days after pollination, and 5, 10, 20 and 30 days after germination using isobaric tags for relative and absolute quantitation (iTRAQ) technology to determine the protein profiles of lipid dynamics during peanut seed development and post germination. A total of 5,712 of 8,505 proteins were identified, quantified and divided into 23 functional groups, the largest of which was metabolism-related. Further analyses of the proteins and their pathways revealed initiation of fatty acid accumulation at early stages after flowering, while lipid degradation occurred largely through the lipoxygenase-dependent pathway. Protein expression patterns related to lipid accumulation and degradation were also verified at transcript levels using quantitative real-time PCR. The proteome profiles determined here will significantly enrich our understanding of the process of lipid accumulation and degradation, and the dynamic changes in metabolic networks during peanut development. Keywords: proteomics, seed development, seed germination, lipid accumulation, lipid degradation

Introduction Peanut (Arachis hypogaea) is a major economic and edible oil crop, the seeds of which contain approximately 46-60% oil 1. As a result, peanut is in great demand both for human consumption and industrial use. Lipids, the major determinant of yield in oil plants, provide energy for seed germination and seedling growth. Understanding the mechanism of lipid accumulation and degradation is therefore essential for breeding new peanut species with a high yield.

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Triacylglycerols (TAGs) are a major form of lipid stored in seeds and fruits2,3. TAGs are hydrolyzed to generate fatty acids and glycerol, which are further broken down by β-oxidation, the TCA cycle and gluconeogenesis successively4. Research on the synthesis of storage oils and their biochemical and metabolic processes will help us understand the accumulation of oil and energy flow during seed development and seedling growth5-9. Seed germination and development are dynamic and complex processes that involve the regulation of important reserve components such as proteins, carbohydrates and oil. During seed germination, reserve components are subjected to dramatic catabolism, providing nutrients and energy for growth and development of the young embryos. Although gene expression and regulation during seed development and germination have been studied extensively, little is known about the dynamic changes in corresponding proteins and enzymes during peanut seed development and germination. Guo et al. and Yin et al. identified the genes involved in lipid accumulation during seed development using transcriptome10,11. Gene transcripts of a number of lipid metabolic enzymes have been identified through analysis of expressed sequence tag (EST) databases, which contain a large number of non-redundant transcript sequences of coding genes10–12 as well as the primary transcripts for non-coding genes such as microRNAs (miRNAs) in peanut13. In our previous study, comparative transcriptomic analysis revealed seven possible metabolic pathways involved in oil accumulation during seed development. Due to the stability of translated proteins/enzymes subjected to various post-translational modifications and/or regulation, precise information on the proteins involved remains limited following transcriptome analysis. Moreover, transcriptome levels often fail to correlate with expressed protein levels14, thereby limiting our understanding of certain metabolic processes such as lipid metabolism. Proteomics is a powerful tool for understanding the complex regulatory mechanisms and dynamics behind protein changes. Proteomic analysis of protein dynamics during seed development and germination have been addressed in Arabidopsis15,16, legumes17, rice and other species18. In peanut, using 2-DE proteomic analysis, Zhu et al. identified several candidate proteins related to pod swelling by comparing the proteomic profiles of aerial and subterranean pods19. Furthermore, Sun et al., using various treatments, identified 27 differentially expressed proteins in aerial gynophores, subterranean gynophores, and gynophores20. Thousands of proteins have also been detected in aerial gynophores, subterranean gynophores, and early swelling peanut pods using 1-DE with nanoLC-MS/MS approaches21. The results of these studies have provided valuable information on peanut pod swelling; however, few proteins related to lipid metabolism have yet been identified. These studies also failed to identify the comprehensive molecular mechanism underlying seed development and germination in 2 ACS Paragon Plus Environment

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peanuts. Isobaric tags for relative and absolute quantitation (iTRAQ) is a mass-based quantitative approach that has recently become prevalent in the field of crop proteomics, allowing simultaneous identification and quantification of proteins from multiple samples with high coverage. Furthermore, iTRAQ combined with tandem mass spectrometry (MS/MS) was also developed to carry out comprehensive profiling of even low-abundance proteins with more accurate quantification22. In this study, for the first time, iTRAQ-based quantitative proteomic analysis was used to understand the mobilization of protein reserves and characteristics of protein expression dynamics during seed development and germination in peanut. Quantitative reverse transcription-PCR (qRT-PCR) was also carried out to correlate and validate the proteomic results related to oil accumulation and degradation. The constructed proteome profiles will significantly promote our understanding of lipid networks and lipid dynamics at different stages of seed development and germination. Materials and Methods Peanut seed growth conditions and treatments The peanut subline cultivar HuaU606, which originated from the parents Yuhua15 (male) and Huayu17 (female), was selected for use in the experiments. Developing seeds were manually collected at different days after flowering (20, 40, 60 and 80 DAF). Mature seeds of U606 were kept at 25°C with 8 h light and 16 h dark daily, in standard tissue culture flasks containing filter paper moistened with distilled water. Germination assays were conducted using three replicates of 40 seeds at each sampling. Cotyledons were collected at different germination stages (5, 10, 20 and 30 DAG), frozen immediately in liquid nitrogen and stored at −80°C until further use. Measurements of oil content and fatty acids Fresh seeds obtained at each development stages were ground into powder in liquid nitrogen. The oil content was then calculated following the Soxhlet extraction method and the fatty acid composition analyzed by gas chromatography-mass spectrometry as described by Yang et al.23 The fresh seeds of different stages were ground into powder in liquid nitrogen. The oil content was calculated following soxhlet extraction method and the fatty acid composition was analyzed as follows: Taking About 0.02 g powder of peanut seeds, and added 100 ul methanol with 0.2% BHT, 100 ul methanol with 0.2% BHT, 200 ul toluene along with 17:0 FA standard. The solution firstly was refluxed at 80°C for 1h. And then added 1.5 ml 0.9% NaCl and 2 ml hexane with 0.2% BHT and shaked for 3 min, the top phase collected for GC after centrifuged at 5000 rpm for 2 min,. The fatty acid methyl esters were 3 ACS Paragon Plus Environment

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separated by gas chromatography (DEGS-diethyl glycol succinate column). The GC conditions were: injector temperature and flame ionization detector temperature, 230°C; keeping oven temperature 160°C for 30min, then increasing at 3°C/min to 230°C and holding at 200°C for 5 min23. The relative FA compositions were calculated by three independent biological replicates.

Protein Extraction and Digestion Protein samples obtained at 20, 40, 60 and 80 DAF, and 5, 10, 20 and 30 DAG were used for proteomic analysis. There were three sample replicates composed of a 10-seed mixture in each one. The cotyledons were ground into a power in liquid nitrogen and the resulting fine powder precipitated using cold 10% TCA/acetone supplemented with 50mM DTT, 2mM EDTA, protease inhibitor cocktail and PVPP powder at -20°C for 2h in a 50-ml centrifuge tube. The extracts were then centrifuged at 20,000g at 4°C for 10 min and the supernatants discarded. The precipitate was washed three times in cold acetone containing 50mM DTT. After air drying, the precipitate was re-suspended in lysis buffer (8M urea, 10mM DTT, 2mM EDTA, and protease inhibitor cocktail) and processed on ice using a high intensity ultrasonic processor (Scientz) then centrifuged at 20,000g at 4°C for 10 min. The supernatants were transferred to a fresh tube and the protein content determined using a 2-D Quant kit (GE Healthcare) following the manufacturer’s instructions. The protein samples were reduced by treating the disulfide bonds with 10 mM DTT for 1 h at 56°C and further alkylated using 55 mM iodoacetamide for 45 min in darkness at room temperature. The treated protein was finally precipitated with 20% TCA for 2 h at 4°C and washed with cold acetone three times. Resulting pellets were dissolved and sonicated using 0.5M TEAB and the protein suspension digested with Trypsin (Promega) at an enzyme substrate ratio of 1:50 at 37°C for 12 h. To ensure complete digestion of the protein suspension, the digested protein solution was added to additional trypsin at a ratio of 1:50 and incubated for a further 4h. ITRAQ Labeling and Fractionation with High-pH Reverse-Phase Chromatography After trypsin digestion, the peptides were desalinized using a Strata X C18 SPE column (Phenomenex) and vacuum dried. Peptides were reconstituted in 1M TEAB following the manufacturer’s instructions using an 8-plex iTRAQ kit (AB Sciex). Peptides were labeled as follows: at 20 DAF with iTRAQ-113, 40 DAF with iTRAQ-114, 60 DAF with iTRAQ-115 and 80 DAF with iTRAQ-116; at 5 DAG with iTRAQ-117, 10 DAG with iTRAQ-118, 20 DAG with iTRAQ-119 and 30 DAG with iTRAQ-121. Briefly, six units of iTRAQ reagent (per 100 µg of protein) was defrosted and reconstituted in 80 µL acetonitrile, and the labeled peptides from each sample incubated for 2 h at room temperature. The 4 ACS Paragon Plus Environment

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peptide mixtures were then collected and vacuum dried. Labeled peptides were further separated with reverse-phase high performance liquid chromatography (HPLC). The reverse-phase column (Agilent, ZORBAX Extended-C18 4.6 mm x 250 mm, 5 µm particle, 80 Å pore size) was equilibrated with 2% buffer B (10 mM ammonium formate and 90% acetonitrile, pH 10.0) and the peptide mixture in buffer A (10 mM ammonium formate in 2% acetonitrile, pH 10.0) loaded onto the column and eluted at a linear gradient of 5 to 8% buffer B for 5 min, 8-18% B for 35 min, 18-32% B for 22 min and 32-95% B for 2 min at a constant flow rate of 1 mL/min using analytical HPLC (Rigol). A total of fifty-six fractions were collected and combined equally into 14 final fractions. LC-ESI-MS/MS Analysis using Q Exactive Peptides from each fraction were vacuum dried and re-suspended in buffer A (0.1% FA, 2% ACN) then centrifuged at 20,000g for 2min. The supernatant was then transferred into a sample tube and using EASY nLC1000 nanoUPLC (Thermo Scientific), loaded onto an Acclaim PepMap 100 C18 trap column (Dionex, 75um×2cm) to elute the peptide onto a second type of Acclaim PepMap RSLC C18 analytical column (Dionex, 50um×15cm). A 34-min gradient was then applied at a flow rate of 300 nl/min from 5 to 30% buffer B (0.1% FA, 80% ACN) followed by a linear gradient to 40% buffer B for 2 min, which was then ramped to 80% buffer B in 2 min, and finally, kept at 80% buffer B for 4 min. After applying the NSI source, the peptides were collected by MS/MS using Q Exactive (Thermo) coupled to UPLC online. Intact peptides were detected in the Orbitrap at a resolution of 70000 at 200

m/z. Using 27% NCE with 12% stepped NCE, peptides were then selected for MS/MS. Ions with charge state 2-5 were allowed for fragmentation in mass spectrometers. Ion fragments were detected in the Orbitrap at a resolution of 17500 at 200 m/z. A data-dependent acquisition procedure was alternated between a single MS scan followed by 20 MS/MS scans and applied to the top 20 precursor ions above a threshold ion count of 3E4 in the MS survey scan with 5.0s dynamic exclusion. The voltage of the spray was 1.8 kV. To prevent overfilling of the ion trap, automatic gain control (AGC) was used, with 1E5 ions accumulated to generate the MS/MS spectra. For MS scans, an m/z scan range of 350 to 1600 Da was used. Normalized collision energy was 30 eV and the underfill ratio was defined as 0.1%. Database Search The instrument data (.raw) were combined and converted into a.mgf using Proteome Discoverer (ver. 1.3.0.339, Thermo). Peptides and protein identifications were made using the Mascot search engine 5 ACS Paragon Plus Environment

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(ver. 2.3.02, Matrix Science). Protein sequence data used for MS/MS search come from Papilionoideae of UniprotKB (157,781 sequences, release time 2013.12.14). Database searches were limited to tryptic peptides. Carbamidomethyl (C), TMT6plex (N-term) and TMT6plex (K) were selected as fixed modifications and oxidation (M) as a variable modification, with 2 missed cleavages allowed, a precursor < 10 ppm, and fragment deviation set at 0.02 Da. The filter criteria of spectra hits given a p value 95% confidence level were accepted. The FDR filter was applied at the peptide level and 1%FDR was used to filter the protein identifications. For quantifications, all quantified peptides in one protein were combined to calculate the p-values. The median of the quantified peptides was used as protein relative quantity. Based on protein ID, Gene Ontology (GO) annotation proteomes were derived from the UniProt-GOA database (http://www.ebi.ac.uk/GOA/). Differentially expressed proteins involved in lipid accumulation and degradation were subsequently clustered via hierarchical clustering and k-means clustering using EXPANDER. qRT-PCR Analysis Cotyledons obtained at 20, 40, 60 and 80 DAF were chosen for qRT-PCR analysis. Total RNA was extracted using the method described by Yin et al.24. RNA concentrations were measured using an ND1000 spectrophotometer and RNA quality visualized using standard agarose gel (1%, w/v). RNase-free DNaseI (Fermentas, USA) was used to remove genomic DNA contaminants, and first-strand cDNA was synthesized using PrimeScript RT Master Mix (TaKaRa, Dalian, China). qRT-PCR followed the manufacturer’s instructions (TaKaRa, Dalian, China; primers are listed in Supplemental Table 1). Reactions were as follows: 95°C for 30s followed by 40 cycles of 95°C for 5s and 60°C for 30s. All reactions were replicated three times for each sample. After amplification, the melting curve and Ct value were used to analyze the success of qRT-PCR. Relative quantification was performed according to the 2−

ΔΔCt

method.

Results Developmental and germinating characteristics of peanut seeds To profile the proteome dynamics during peanut seed formation and germination, we documented the developmental and germinating characteristics of peanut seeds in detail. Peanut needles elongate into the soil and then rapidly swell under suitable conditions. Initially, the pod developed into a chicken’s head shape, full of white spongy tissues. The pods then swelled rapidly into kernels, with an increase in size and decrease in spongy tissue, before finally developing into underground mature pods containing mature seeds (Figure 1A). After dormancy, and after absorbing sufficient moisture (5 h imbibition), the 6 ACS Paragon Plus Environment

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mature seeds started to germinate. The hypocotyl then elongated, the radicle broke through the testa, the cotyledon opened, and the germ started to grow. After germination, the radicle grew downwards quickly forming taproots at 5 DAG (Figure 1A), and lateral roots started to form. Finally, the peanut germs developed into seedlings with functional stems, leaves and roots. Component analysis and fatty acid (FA) composition Next, we analyzed the major components, in particular the fatty acid composition, of the developing and germinating seeds at each stage. Protein and lipid contents were determined at 20, 40, 60 and 80 DAF and 5, 10, 20 and 30 DAG). As a result, significant changes in the oil content of the cotyledon were revealed (Figure 1B). The oil content followed an "S" curve increase during development; however, during post germination stages, a sharp decrease was observed from 60.7% in the mature period to 15.19% at 30 DAG. This suggests mobilization and consumption of the lipids during seed germination and post germination stages. The FA composition of the crude lipids at each stage were further determined in detail (Figure 1C). Among the identified FAs, two fatty acids, oleic acid (C18:1) and linoleic acid (C18:2), occupied the highest percentage followed by palmitic acid (C16:0) and stearic acid (C18:0). During seed developmental, the relative contents of oleic acid and stearic acid consistently increased. In contrast, the relative content of palmitic acid was high (17.82%) at 20 DAF and then gradually decreased till maturity, while relative contents of linoleic acid, arachidic acid, and 24 carbon acids remained almost constant throughout the various development stages. Linolenic acid started to reduce quickly at an early stage and was almost undetectable at 40 DAF. In addition to the nine fatty acids mentioned above, a new fatty acid (C22:6) was also detected at the germination stage. At first, C22:6 increased rapidly, but then decreased quickly up till 30 DAG at which point it was undetectable. During germination, linolenic acid reappeared at a low level. The relative content of C20:1 and C24:0 increased slowly at early germination stages then decreased rapidly to zero. The contents of the remaining six fatty acids (C16:0, C18:0, C18:1, C18:2, C20:0 and C22:0) changed only slightly during germination. Functional identification and classification of proteins identified by iTRAQ Proteins extracted from the cotyledon were initially separated by one-dimensional SDS-PAGE electrophoresis to reduce the protein complexity (Supplemental Figure 1). Protein bands showed obvious differences among samples such as those at 20 DAF, which showed protein bands obviously different. That is, the proteins observed during development changed remarkably at germination. A total of 16,271 peptides were matched. The length of most (about 70%) was in the range of 8-17aa. 7 ACS Paragon Plus Environment

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To determine dynamic changes in the proteomic profiles at each stage of development, relative expression levels of the proteins at 20, 40 and 60 DAF, and 5, 10, 20 and 30 DAG were investigated, with those obtained at 80 DAF as a control. ITRAQ proteomic analysis identified a total of 8,505 proteins across the different stages and, of these, at least 5,712 were quantified. Gene ontology (GO) categorization of the identified peptides was subsequently carried out to further categorize the functions of the identified proteins (Supplemental Figure 2). Cellular functions were assigned to the following cellular compartments: cell parts (40.26%), organelles (17.6%), organelle parts (15.87%) and extracellular regions (14.29%) (Supplemental Figure 2C). Biological functions were associated with the following brief pathways: metabolic processes (68.59%), biological adhesion (8.96%), cellular processes (8.93%) and immune system processes (4.45%) (Supplemental Figure 2A). The most highly enriched molecular functions in the peanut seeds were catalytic proteins (68.64%), followed by binding (18.82%) and structural proteins (9.41%), respectively (Supplemental Figure 2B). The 5,712 quantified proteins were further classified into 23 functional categories using clusters of orthologous group (COG) classifications (Figure 2). The largest group category was found to have general functions only (11.81%) followed by proteins related to DNA replication, recombination and repair (8.49%), and transcription (8.42%). Only a small fraction of the proteins were functionally related to the categories of “RNA processing and modification” (0.35%) and “cell motility” (0.05%). About 2.6% of the identified proteins (112 proteins) were related to lipid metabolism. Protein expression profiles of lipid pathways at different stages of peanut development Mature seeds of U606 contain approximately 60% oils, most of which are stored in oil bodies as TAGs. Approximately 241 of the identified proteins were found to be involved in lipid metabolism (Supplemental Table 2), 88 of which were differentially expressed (> 2-fold or < 0.5-fold; p