Abstract Wheat (Triticum aestivum L.) is an important crop worldwide. The physiological deterioration of seeds during storage and seed priming is closely associated with germination, and thus contributes to plant growth and subsequent grain yields. In this study, wheat seeds during different stages of artificial ageing (45°C; 50% relative humidity; 98%, 50%, 20%, and 1% Germination rates) and priming (hydro-priming treatment) were subjected to proteomics analysis through a proteomic approach based on the isobaric tandem mass tag labeling. A total of 162 differentially expressed proteins (DEPs) mainly involved in metabolism, energy supply, and defense/stress responses, were identified during artificial ageing and thus validated previous physiological and biochemical studies. These DEPs indicated that the inability to protect against ageing leads to the incremental decomposition of the stored substance, impairment of metabolism and energy supply, and ultimately resulted in seed deterioration. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the up-regulated proteins involved in seed ageing were mainly enriched in ribosome, whereas the down-regulated proteins were mainly accumulated in energy supply (starch and sucrose metabolism) and stress defense (ascorbate and aldarate metabolism). Proteins, including hemoglobin 1, oleosin, agglutinin, and non-specific lipid-transfer proteins, were first identified in aged seeds and might be regarded as new markers of seed deterioration. Of the identified proteins, 531 DEPs were recognized during seed priming compared with unprimed seeds. In contrast to the up-regulated DEPs in seed ageing, several up-regulated DEPs in priming were involved in energy supply (tricarboxylic acid cycle, glycolysis, and fatty acid oxidation), anabolism (amino acids, and fatty acid synthesis), and cell growth/division. KEGG and protein-protein interaction analysis indicated that the up-regulated proteins in seed priming were mainly enriched in amino acid synthesis, stress defense (plant-pathogen interactions, and ascorbate and aldarate metabolism), and energy supply (oxidative phosphorylation and carbon metabolism). Therefore, DEPs associated with seed ageing and priming can be used to characterize seed vigor and optimize germination enhancement treatments. This work reveals new proteomic insights into protein changes that occur during seed deterioration and priming. Introduction Wheat (Triticum aestivum L.), one of the most important, oldest and widely cultivated crops, is a staple food source for humans and livestock feed worldwide because of its high nutritional value [[30]1, [31]2]. As orthodox type seeds, wheat seeds undergo desiccation after maturation, which enables to survive for a long time in a metabolic standstill situation [[32]3]. As storage time is prolonged, seed vigor gradually decreases, and the germination rate eventually diminishes; as a consequence, commercial and genetic losses occur [[33]4, [34]5]. Hence, seed ageing and germination mechanisms should be understood to develop new measures for seed conservation and production. Seed ageing causes the physiological deterioration of seeds, which includes a reduced germination rate and an increased post-germination growth time [[35]6, [36]7]. At present, the altered physiological and biochemical characteristics of seeds have been extensively investigated to elucidate seed aging mechanisms [[37]8, [38]9]. Seed deterioration is mainly influenced by the accumulation of reactive oxygen species, lipid peroxidation mediated by free radicals, disruption of cellular membranes, and damage to proteins and nucleic acids [[39]7, [40]8, [41]10–[42]15]. Proteomic studies on artificially-aged Arabidopsis and Zea mays (maize) seeds indicated that differentially expressed proteins (DEPs) are mainly involved in oxidative stress, metabolism, and energy supply, which indicated that the proteomic changes can occur during deterioration at the dry state of aged seeds [[43]4, [44]7]. Das and Sen-Mandi [[45]16] further revealed that the physiological deterioration of wheat begins in its embryo, and this phenomenon is correlated with germination. Nevertheless, the mechanism underlying artificial ageing of wheat seeds remains unknown. Crop seed germinability is a vital factor that contributes to seedling performance, plant establishment, and subsequent crop development and growth. Seed germination is controlled by both internal and external factors, including genetics, seed structure, seed chemistry, humidity, and temperature [[46]17]. To improve and synchronize seed germination and emergence, researchers apply seed invigoration treatments called seed priming. Seed priming involves pretreatments with water and various chemical reagents, including polyethylene glycol, ascorbic acid, hormones, and vitamins [[47]18, [48]19, [49]20, [50]21]. Proteomic investigations have been conducted during the seed germination of several plant seeds, such as wheat [[51]22, [52]23], alfalfa [[53]21], Arabidopsis [[54]3], and maize [[55]24]. These proteomic studies, conducted using two-dimensional (2-D) electrophoresis [[56]21, [57]22] and 2-D differential gel electrophoresis [[58]23], have provided critical information on the metabolic process of seed germination. However, 2-D-gel-based approaches suffer from low reproducibility and under-representation of low abundance and hydrophobic proteins [[59]25]. These limitations can be overcome by a non-gel-based quantitative proteomic approach using isobaric tagging reagents. Isobaric tagging reagents, such as tandem mass tags (TMT) and isobaric tags for relative and absolute quantification (iTRAQ), have been developed for mass spectrometry (MS)-based protein detection and quantification in complicated samples [[60]26, [61]27]. For instance, iTRAQ has been applied to conduct a quantitative proteomics study on wheat grain development and drought response [[62]28, [63]29]. However, quantitative proteomics studies on wheat seed priming have yet to be reported. The Chinese wheat cultivar ‘Aikang58’, a medium-hard wheat widely cultivated in the main wheat production areas of China, exhibits excellent characteristics, including drought tolerance, freeze resistance, wide adaptability, and high yield [[64]30]. In this work, the first TMT-based quantitative proteome analysis of elite Chinese wheat cultivar ‘Aikang58’ seeds was conducted during artificial ageing and priming. We uncovered new information on the proteomic changes during ageing and priming in wheat seeds that might provide new insights into metabolic pathways, as well as into adverse defense mechanisms during seed deterioration and priming. Materials and Methods Wheat seeds, artificial ageing and priming treatment The elite Chinese bread wheat cultivar ‘Aikang 58’ seeds used in this study were purchased from the Henan Academy of Agricultural Science. Seeds with similar sizes and weights were selected, and the germination rate (Gr) was calculated in accordance with the methods proposed by Dong et al. [[65]23]. The original Gr was 98.0% (designated as WH98), and the seed moisture content was 9.79%. Seeds were artificially aged by sealing them in air-tight plastic bottles and then stored at 45°C (± 1°C) and 50% relative humidity in a constant-temperature- and humidity-controlled cabinet (Binder KMF720, Tuttingen, Germany) in accordance with previous described methods [[66]4] with minor modification. The vigor of the wheat seeds was determined regularly. The seed samples with three biological replicates were collected when Gr were 50%, 20%, and 1% (designated as WH50, WH20, and WH01 respectively). The collected samples were stored at -80°C prior to analysis. Unaged seeds (WH98) were used as control specimens. Hydro-priming treatment was based on a previous method [[67]3]. In brief, hydro-primed seeds (designated as WH100) were prepared by immersing dry mature seeds in sterilized water for 8 h at 25°C, packed in a wet gauze, and incubated for 12 h at 25°C. Scanning electron microscopy (SEM) SEM analysis was performed on the basis of a previous method [[68]31]. WH98, WH50, WH20, and WH01 were halved vertically to the ventral side by using a cryostat (Leica CM1950, Solms, Germany). The cut side of embryo of the aged seeds was examined under a SEM (Quanta 250 FEG, FEI, Hillsboro, OR, USA). Protein extraction and trypsin digestion Embryo samples were collected by the dissection of WH98, WH50, WH20, WH01, and WH100 seeds as previously described [[69]32], with each having three biological replicates. Afterward, the embryo samples of the wheat seeds were sonicated thrice on ice using a high intensity ultrasonic processor (Scientz, Ningbo, China) in lysis buffer (8 M urea, 1% Triton-100, 10 mM dithiothreitol and 1% protease inhibitor cocktail VI). After the samples were centrifuged at 20,000 × g for 10 min at 4°C, the supernatant was precipitated with 15% cold trichloroacetic acid for 2 h at -20°C and then centrifuged for 10 min at 4°C. The obtained precipitate was washed with cold acetone thrice, and re-dissolved in buffer (8 M urea and 100 mM triethylammonium bicarbonate, pH 8.0). Protein concentration was determined by using a 2-D Quant kit (GE Healthcare) according to the manufacturer’s instructions, and then stored at -80°C for further use. Approximately 100 μg of proteins for each sample was digested with trypsin for the subsequent experiments. In briefly, proteins from wheat seed embryos were reduced with 10 mM dithiothreitol for 1 h at 37°C and alkylated with 20 mM indole-3-acetic acid for 45 min at room temperature in the dark. Finally, trypsin was added at 1:50 trypsin:protein mass ratio for the first digestion overnight and at a 1:100 trypsin:protein mass ratio for a second 4-h digestion. TMT labeling and high-performance liquid chromatography (LC) fractionation After trypsin digestion was completed, peptides were desalted using a Strata X C18 SPE column (Phenomenex, Torrance, CA, USA) and vacuum-dried. Peptides were reconstituted in 0.5 M triethylammonium bicarbonate and labeled. In brief, one unit of TMT reagent (labeled 100 μg of protein) was thawed and reconstituted in 24 μl acetonitrile. The peptide mixtures were incubated for 2 h at room temperature, pooled, desalted, and dried through vacuum centrifugation. Five samples with three biological replicates were labeled with TMT tags. WH100 and WH98 were labeled with 130 and 129 respectively. WH50, WH20, and WH01 were labeled with 128, 127 and 126respectively. The TMT labeled samples were then fractionated through high pH reverse-phase high-performance liquid chromatography (LC) by using Agilent 300 Extend C18 columns (5 μm particles, 4.6 mm ID, 250 mm length). The LC gradient was run with 2% to 60% acetonitrile in 10 mM ammonium bicarbonate (pH 10) for 80 min to generate 80 fractions. Afterward, which all of the fractions were combined into 18 fractions. The fractionated samples were dried through vacuum centrifugation and stored at -20°C. LC-MS/MS analysis LC-MS/MS was performed on the basis of a previous report [[70]33]. In brief, the peptides were dissolved in 0.1% formic acid, directly loaded onto a reversed-phase pre-column (Acclaim PepMap 100, Thermo Fisher Scientific, Waltham, MA, USA), and separated using a reversed-phase analytical column (Acclaim PepMap RSLC, Thermo Fisher Scientific). The gradient was increased from 7% to 20% solvent B (0.1% formic acid in 98% acetonitrile) for 24 min, from 20% to 35% in 8 min, and from 35% to 80% in 5 min. The gradient was then maintained at 80% for the last 3 min. A constant flow rate of 280 nl/min was set in an EASY-nLC 1000 UPLC system. The resulting peptides were analyzed by using Q Exactive^™ hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific). The peptides were subjected to a nanospray ion source followed by tandem mass spectrometry (MS/MS) in Q Exactive^™ (Thermo Fisher Scientific) coupled online to ultra performance LC. The peptides were then selected for MS/MS by using the NCE settings of 27, 30, and 33. A data-dependent procedure that alternated between one MS scan followed by 20 MS/MS scans was applied to the top 20 precursor ions above a threshold ion count of 1.0E4 in the MS survey scan with a 30.0 s dynamic exclusion. The fixed first mass was set at 100 m/z. The electrospray voltage and m/z scan range were described previously [[71]33]. Database search and bioinformatics analysis The resulting MS/MS data were processed using Mascot search engine (v.2.3.0 [72]http://www.matrixscience.com/). Tandem mass spectra were used as query in the Uniprot_Triticum_aestivum database (100,981 sequences). Trypsin/P was specified as the cleavage enzyme that allows up to two missing cleavage sites. The mass error was set to 10 ppm for precursor ions and 0.02 Da for fragment ions. Carbamidomethyl on cysteine, TMT-6plex (N-term), and TMT-6 plex (K) were specified as fixed modifications, and oxidation on methionine was specified as a variable modification. The false discovery rate was adjusted to < 1%, and the peptide ion score was set at > 20. The proteins displaying a 1.2 fold change between artificially aged and normal seeds (WH01 vs WH98, WH20 vs WH98 and WH50 vs WH98) and between priming and normal seeds (WH100 vs WH98) were considered as DEPs if p < 0.05. Protein annotation and functional analysis Gene ontology (GO) ([73]http://www.geneontology.org) and Kyoto Encyclopedia of Genes and Genomes (KEGG) ([74]http://www.genome.jp/kegg/pathway.html) analyses were conducted in accordance with previously reported methods [[75]34]. Proteins were subjected to a eukaryotic orthologous group (KOG) analysis by performing a homology search of the KOG database with the following parameters: E value < 1e-5, identities > 80%, and percent of match length > 60%. A domain annotation was performed using InterProScan on the InterPro ([76]http://www.ebi.ac.uk/interpro/) domain database via Web-based interfaces and services [[77]35]. Subcellular localization was determined by Wolfpsort (version of PSORT/PSORT II, [78]http://psort.hgc.jp/). GO, KEGG pathway, and protein domain enrichment analyses were performed, and a two-tailed Fisher’s exact test was employed to examine the enrichment of the DEPs against all of the identified proteins. Multiple hypothesis testing was corrected by using standard false discovery rate control methods, and domains with a corrected p-value < 0.05 were considered significant. For hierarchical clustering based on different protein functional classifications (GO, domain, pathway), proteins from the categories were obtained after enrichment and then the categories were filtered to identify those that were at least enriched in one of the clusters with p-value < 0.05. This filtered p-value matrix was transformed by using the function x = −log10 (p-value), and x values were z-transformed for each functional category. Cluster membership was visualized by using a heat map via the “heatmap.2” function from “gplots” R-package. Protein-protein interaction (PPI) network All of the DEPs were used as query against the STRING database (version 10.0, [79]http://string-db.org) [[80]36] to identify protein-protein interactions. STRING defines a metric called “confidence score” to describe the interaction confidence. We obtained all of the interactions with a confidence score ≥ 0.7 (high confidence). The interaction network from STRING was visualized with Cytoscape ([81]http://www.cytoscape.org/) [[82]37]. Molecular complex detection (MCODE) was utilized to analyze densely connected regions. MCODE is part of the plug-in toolkit of the network analysis and visualization software Cytoscape. RNA isolation and quantitative real-time PCR (qRT-PCR) analysis Total RNA from wheat embryos of WH98, WH50, WH20, and WH01 were extracted by using RNAiso Plus reagent (Takara, Tokyo, Japan), and genomic DNA was removed by treating with DNase I (Takara) following the manufacturer’s protocol. Reverse transcription was performed by using a PrimeScriptRT reagent kit (Takara, RR047A) with a random primer mix in accordance with the manufacturer’s instructions. Primer pairs for qRT-PCR analysis ([83]S3 Table) were designed by Primer5 and checked by querying the primer sequences, using the BLAST algorithm against the NCBI database. All of the primers were consistent with their respective target gene sequences. qRT-PCR of the translationally-controlled tumor protein, (TCTP; [84]Q8LRM8), asparagine synthetase (AS; W5CK94) and catalase (CAT; W5HND1) genes was performed using Mastercycler ep realplex (Eppendorf, Hamburg, Germany) and SYBR Premix Ex Taq (Takara, RR420A) with ADP-ribosylation factor as a reference gene [[85]38]. Results Germinability and microstructural changes during artificial ageing of wheat seeds embryo To investigate seed vigor and structural changes in embryos, Gr and SEM were employed to evaluate the alterations. There was no obvious difference in the Gr of seeds stored at room temperature for nearly 210 days, while the artificial ageing treatment resulted in a decreased germinability from 98% to 50% (65 d), 20% (131 d), and 1% (210 d). Gr was decreased rapidly from 30 (85%) to 90 d (41%), and almost no seeds could germinate after being stored 210 d (1%) at 45°C ([86]Fig 1A). Gr also indicated that the deterioration rate accelerated after 30 d at 45°C. After priming was performed, the radicle protruded through the seed coat and the embryo was collected for further analysis ([87]S1A Fig). Fig 1. Wheat seed development during the artificial ageing of cultivar ‘Aikang58’. [88]Fig 1 [89]Open in a new tab (A) Seed germination during artificial ageing. (B) Scanning electronic microscope observations of embryos from artificial ageing grains. The SEM images (magnification was 4000×) of the cut side of embryos during artificial ageing were shown in [90]Fig 1B. The results indicated that artificially aged seeds (WH50, WH20, and WH01) showed a degradation of granules and fold change on the surface compared with those of WH98. The altered microstructure and decreased Gr suggested that various physiological reactions might occur during this phase. Overview of quantitative proteomics analysis In this study, the TMT-based quantitative proteomic characteristics of the ‘Aikang58’ cultivar were investigated to uncover the altered proteins involved in artificial deterioration and priming. A global profile of the quantitative proteome was obtained by using the embryos from WH98, WH50, WH20, WH01 and WH100, with three biological replicates each. The SDS-PAGE analysis of proteins from the embryo was shown in [91]S1B Fig. Each lane was loaded with 10 μg of proteins. When samples were subjected to an LC-MS/MS analysis, the data validation was evaluated. The distribution of mass error is near zero and most errors are less than 0.02 Da. These findings indicated that the MS data’s accuracy met the requirement ([92]S1C Fig). The lengths of most of the peptides were distributed between 8 and 16 aa, which was consistent with the properties of tryptic peptides ([93]S1D Fig), indicating that the sample preparation met the standard. A total of 6281 proteins were identified, of which 3574 proteins with quantitative information were elicited from T. aestivum in the three biological replicates ([94]S1 Table), and the coverage of identified proteins among the three biological repeats are also presented ([95]S2A Fig). The protein expression levels were comparatively analyzed and divided into two groups: protein changes occurring during artificial ageing (WH01 vs WH98, WH20 vs WH98, and WH50 vs WH98), and protein changes occurring during priming (WH100 vs WH98). Pearson’s correlation coefficient was obtained to evaluate the repeatability of protein relative quantitation. The pair-wise Pearson’s correlation coefficients of the samples (protein changes during artificial ageing) are presented in a red-white-green heat map format ([96]S2B Fig) and the reproducibility of the protein quantitation (protein changes during priming) of the samples is presented ([97]S2C Fig). A 45°-diagonal line was obtained throughout the detection range, and this finding indicated the expected distribution without obvious changes among the three biological replicates (artificial ageing). The correlation coefficient was > 0.82 among the three biological replicates (priming), which indicated good reproducibility. All the identified proteins were annotated ([98]S2 Table), including GO, KEGG, KOG, and domain annotations. Of the 3574 quantified proteins, 162 were identified as DEPs, containing 36 up-regulated (≥1.2-fold, p-value ≤ 0.05) and 126 down-regulated (≤ 0.83-fold, p-value ≤ 0.05) in at least one of the artificial ageing stages compared with unaged seeds ([99]Fig 2A, [100]S3 Table). The 36 up-regulated proteins included 16 DEPs at WH01, 18 DEPs at WH20, and 10 DEPs at WH50, of which 8 DEPs were at two stages, meanwhile the 126 down-regulated proteins included 80 DEPs at WH01, 40 DEPs at WH20, and 57 at WH50, of which 10 DEPs were shared by all of the artificial ageing stages and 32 DEPs were shared by two stages (12 at WH01 and WH20, 12 at WH01 and WH50, and 8 at WH20 and WH50). Fig 2. Differentially expressed proteins and their functional classification analysis during artificial ageing. [101]Fig 2 [102]Open in a new tab (A) Differentially expressed proteins during artificial ageing compared with unaged seeds; (B) sub-cellular localization analysis; (C) eukaryotic orthologous group (KOG) analysis. Subcellular localization revealed the following: 31% of DEPs in the chloroplast, 22% of DEPs in the cytosol, 21% of DEPs in the nucleus, 11% of DEPs in the extracellular, and 9% of DEPs in the mitochondria ([103]Fig 2B). The KOG annotation showed that DEPs were mainly involved in translation, ribosomal structure and biogenesis (13%), post-translational modification, protein turnover, chaperones (10%), energy production and conversion (9%), and signal transduction mechanisms (9%) ([104]Fig 2C). GO analysis demonstrated that DEPs were mainly involved in binding, metabolic process, catalytic activity, and cellular process ([105]S3A Fig). To further understand the DEPs during artificial ageing, a functional enrichment analysis was performed ([106]S4 Table). A Fisher’s exact test p-value was obtained to evaluate the enrichment analysis with those of the quantified proteins as references. Our results indicated