Breeding Science
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Research Papers
ITRAQ-based quantitative proteomic analysis of japonica rice seedling during cold stress
Dongjin QingGuofu DengYinghua PanLijun GaoHaifu LiangWeiyong ZhouWeiwei ChenJingcheng LiJuan HuangJu GaoChunju LuHao WuKaiqiang LiuGaoxing Dai
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Supplementary material

2022 Volume 72 Issue 2 Pages 150-168

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Abstract

Low temperature is one of the important environmental factors that affect rice growth and yield. To better understand the japonica rice responses to cold stress, isobaric tags for a relative and absolute quantification (iTRAQ) labeling-based quantitative proteomics approach was used to detected changes in protein levels. Two-week-old seedlings of the cold tolerant rice variety Kongyu131 were treated at 8°C for 24, 48 and 72 h, then the total proteins were extracted from tissues and used for quantitative proteomics analysis. A total of 5082 proteins were detected for quantitative analysis, of which 289 proteins were significantly regulated, consisting of 169 uniquely up-regulated proteins and 125 uniquely down-regulated proteins in cold stress groups relative to the control group. Functional analysis revealed that most of the regulated proteins are involved in photosynthesis, metabolic pathway, biosynthesis of secondary metabolites and carbon metabolism. Western blot analysis showed that protein regulation was consistent with the iTRAQ data. The corresponding genes of 25 regulated proteins were used for quantitative real time PCR analysis, and the results showed that the mRNA level was not always parallel to the corresponding protein level. The importance of our study is that it provides new insights into cold stress responses in rice with respect to proteomics and provides candidate genes for cold-tolerance rice breeding.

Introduction

Rice (Oryza sativa) is one of the most important food crops in the world, feeding about half of the population (Khush 2005). Climate is an important factor affecting rice yield, especially low temperatures, which affect rice growth in tropical and subtropical areas, throughout vegetative to reproductive stages (Jin et al. 2018). Low temperature can cause severe injury in seedlings of cold-sensitive rice cultivars in the early season, and reduce growth rate and cause pollen sterility in the late season (Cui et al. 2005, Nijat et al. 2004). Low temperature stress occurs frequently and has wide-ranging influence in the world with the increase in global climate anomalies (Cen et al. 2018, Pradhan et al. 2016). Therefore, it is important to explore genes/proteins that are regulated by low temperature in order to understanding the cold-tolerance mechanism and breeding cold-tolerant rice cultivars.

An increasing number of molecular genetic studies have elucidated how rice plants respond to low temperature stress, as well as the genes/proteins involved in the response. Low temperature is first perceived by the temperature sensor COLD1/RGA1 complex on the plasma membrane, then the complex triggers calcium influx, reactive oxygen species (ROS) production, ABA accumulation and a MAPK cascade (OsMKK6-OsMPK3) leading to active downstream transcription factors responses in the nucleus (Guo et al. 2018, Ma et al. 2015, Manishankar and Kudla 2015). Recently, the vitamin E-vitamin K1 sub-network of the COLD1 downstream pathway was found to be responsible for chilling tolerance divergence (Luo et al. 2021). Other components of cold tolerance have been identified recently, such as CTB4a, which interacts with a beta subunit of ATP synthase AtpB to mediate the ATP supply in rice plant cells to improve cold tolerance (Zhang et al. 2017). The standing variation of cold tolerance gene CTB2 and de novo mutation of CTB4a facilitate cold adaptation of rice cultivation from high altitude to high latitude areas (Li et al. 2021); bZIP73Jap in japonica rice cultivars interacts with bZIP71 to modulates abscisic acid (ABA) levels and reactive oxygen species (ROS) homeostasis for enhancing rice tolerance to cold climate (Liu et al. 2018); rice OsMADS57 interacts with OsTB1 and both directly target OsWRKY94 and D14 for adaptation to cold (Chen et al. 2018). At the rice seedling stage, the cold tolerance associated gene qCTS-9 was identified in hybrid rice under different cold environments using QTL mapping and genome-wide expression profiling methods (Zhao et al. 2017). qPSST6, found from cold tolerance japonica rice variety Kongyu131with QTL mapping and Seq-BSA approach, was validated to be a functional gene relative to cold resistance (Sun et al. 2018). Three genes (LOC_Os01g55350, LOC_Os01g55510 and LOC_Os01g55560) (Zhang et al. 2018) and 67 QTLs (Wang et al. 2016), which were identified by genome-wide association analysis (GWAS), were associated with cold tolerance of indica and japonica rice, respectively. In an RNA-seq comparative analysis of cold-stressed post-meiotic anther from cold-tolerant and cold-susceptible rice cultivars, a number of ethylene-related transcription factors were found to be putative regulators of cold responses (González-Schain et al. 2019).

Proteomics is a robust approach for the large-scale identification of proteins and has been used for profiling proteins in rice (Agrawal and Rakwal 2011). Two-dimensional gel electrophoresis (2-DE) was used to separate proteins of rice treated with cold, and cold responsive proteins were identified using mass spectrometry analysis in early proteomics studies (Hashimoto and Komatsu 2007, Huo et al. 2016, Imin et al. 2006, Ji et al. 2017, Yan et al. 2006). iTRAQ is a powerful mass spectrometry technology, which can quantify proteins’ relative expression abundance by measuring relative peak areas of MS/MS mass spectra of iTRAQ-labeled peptides (Ross et al. 2004). More and more cold response proteins in rice have been monitored and characterized by the iTRAQ-labeling approach. For example, differentially expressed proteins in cold stress-treated rice that are involved in photosynthesis, metabolism, transport, ATP synthesis, ROS, stress response, DNA binding and transcription, and cell growth and integrity, as well as unknown function proteins were found using iTRAQ labeling coupled with LC-MS/MS (Cen et al. 2018, Neilson et al. 2011, Wang et al. 2018a). Although some cold stress responsive proteins were identified by the proteomic approach in different rice cultivars, only a small number of cold-response proteins have been identified so far.

In this study, to better understand the cold tolerance mechanism of japonica rice, we employed the iTRAQ labeling proteomics method to investigate the proteomic response of cold stress of the japonica cold-resistant rice cultivar Kongyu131. Rice seedling tissues were harvested after exposure to 8°C low temperature condition for 0, 24, 48 and 72 h. iTRAQ was used for quantifying relative protein abundance, and different expression proteins were obtained at each time point by comparing to the control samples. Our results report on a large number of cold stress-regulated proteins that have not been previously identified.

Materials and Methods

Plant material and cold stress treatments

The japonica rice variety Kongyu131, which is strongly resistant to cold weather and widely planted in the northeast area of China was used in this study. Rice seedlings were grown in the growth chamber with a 16-h light (28°C)/8-h dark (25°C) condition for 2-weeks. Cold tress treatments were performed by decreasing the temperature to 8°C, and then tissues were collected and frozen in liquid nitrogen at 0 h, 24 h, 48 h and 72 h respectively, and stored at –80°C until protein extraction. For physiology experiments, 2-week-old rice seedlings were separated into groups and treated at 8°C for 4 days, and then transferred to the normal growth temperature for 3 days. For survival rate determination, the ratio of surviving plants to total plants was calculated.

Protein extraction, digestion and iTRAQ labeling

Protein extraction was performed according to previous methods (Guo and Li 2011, Qing et al. 2016, Wang et al. 2018b) with some modifications. The frozen rice seedling tissue (0.5 g) was ground to a fine powder with mortar and pestle pre-chilled at –80°C. The tissue powder was extracted with 5 volumes (g/mL) of extraction buffer containing: 8 M urea,150 mM Tris-HCl, pH 7.6, 1.2% Triton X-100, 0.5% SDS, 5 mM ascorbic acid, 20 mM EDTA, 20 mM EGTA, 5 mM DTT, 50 mM NaF, 1 mM PMSF, 1% glycerol 2-phosphate, 1× protease inhibitor (complete EDTA free; Roche) and 2% polyvinylpolypyrrolidone. The extract was centrifuged at 110,000 × g for 2 h at 10°C to remove debris at the bottom of centrifuge tube. The total protein in the supernatant was precipitated with 3 volumes of –20°C pre-cooled acetone:methanol (12:1 v/v) for at least 2 hours. The protein pellet was collected by centrifugation at 11,000 × g for 20 min and washed two times with acetone:methanol (12:1 v/v), then re-suspended in re-suspension buffer (100 mM Tris-HCl, pH 8.0, 8 M urea). The concentration of total protein was measured by the Bradford method and the proteins sample was used for proteomics and western blot analysis.

Protein samples (200 μg) were reduced by adding 10 mM DTT and incubating at 56°C for 1 h, followed by an alkylation reaction by adding 40 mM iodoacetamide and incubating at room temperature for 30 min in the dark. To digest proteins with trypsin, urea was diluted below 2 M using 100 mM Tris-HCl (pH 8.0), then trypsin was added in the protein solution at 1:50 ratio (enzyme:protein, w/w) and incubated at 37°C overnight. Peptides were acidized by adding formic acid to end the digestion, and then centrifuged at 12,000 × g for 15 min. The supernatant was subjected to peptide purification using a Sep-Pak C18 desalting column. The peptide eluate was vacuum-dried and stored at –20°C.

For iTRAQ labeling, 100 μg of the peptide samples was used. Samples were separately labeled with different iTRAQ labeling reagents (113, 114, 115, 116) according to the manufacturer’s instructions. The labeled samples were mixed and subjected to Sep-Pak C18 desalting, then the complex mixture was fractionated using high pH reverse phase chromatography, and combined into 15 fractions. Each fraction was vacuum-dried and re-suspended in 0.1% formic acid for MS analysis.

LC-MS/MS analysis, and protein quantification

LC-MS/MS detection was carried out on a hybrid quadrupole-TOF LC-MS/MS mass spectrometer (TripleTOF 5600, SCIEX) equipped with a nanospray source. Peptides were first loaded onto a C18 trap column (5 μm, 5 × 0.3 mm, Agilent Technologies) and then eluted into a C18 analytical column (75 μm × 150 mm, 3 μm particle size, 100 Å pore size, Eksigent). Mobile phase A (3% DMSO, 97% H2O, 0.1% formic acid) and mobile phase B (3% DMSO, 97% ACN, 0.1% formic acid) were used to establish a 100 min gradient comprised of: 0 min in 5% B, 65 min of 5–23% B, 20 min of 23–52% B, 1 min of 52–80% B, 80% B for 4 min, 0.1 min of 80–5% B, and a final step in 5% B for 9.9 min. A constant flow rate was set at 300 nL/min. For IDA mode analysis, each scan cycle consisted of one full-scan mass spectrum (with m/z ranging from 350 to 1500, ion accumulation time 250 ms) followed by 40 MS/MS events (m/z ranging from 100 to 1500, ion accumulation time 50 ms). The threshold for MS/MS acquisition activation was set to 120 cps for +2~+5 precusors. Former target ion exclusion was set to 18 s.

Raw data from TripleTOF 5600 was analyzed with ProteinPilot (V4.5) using the Paragon database search algorithm and the integrated false discovery rate (FDR) analysis function. Spectra files were searched against the UniProt japonica rice reference proteome database using the following parameters: Sample Type, iTRAQ 8plex (Peptide labeled); Cys Alkylation, Iodoacetamide; Digestion, Trypsin; Quantitate, Bias correction, and Background correction was enabled for Specific Processing; Search Effort was set to Rapid ID. Search results were filtered with unused score and false discovery rate threshold (FDR) at 1%. Decoy hits were removed, the remaining identifications were used for quantification. Proteins with a fold change of >1.2 or <0.83 and a p-value of <0.05 were considered to be differentially expressed (Fan et al. 2016).

Bioinformatics analysis of DEPs

All DEPs were used for hierarchical cluster analysis with the Cluster 3.0 program. The DEPs were classified and grouped into different pathways according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction networks were analyzed using the STRING 10 database (https://string.embl.de).

Quantitative real time PCR and western blot analysis

To validate the MS quantification results at the transcript level, total rice seedling tissue mRNA extracted using TRIzol reagent (Invitrogen) was used for cDNA synthesis using SuperScritRIII RT First Strand Synthesis Kit (Invitrogen) according to its protocol. SYBR® Premix Ex TaqTM (Takara, China) was used for real-time RT-PCR, and the specific primers (Supplemental Table 1) for target genes amplification were designed using Primer Express 3.0 software. β-actin was used as an internal control gene. Three biological repeats were performed for each target gene in real-time RT-PCR.

Western blot analysis was performed according to Qing et al. (2016). The rabbit polyclonal antibody was raised against synthetic oligopeptides which were identified by MS, DAGDAAPPAAATTTER to make anti-A0A0N7KH91 polyclonal antibody. The peptide antibody was made commercially (GL Biochem Co., Ltd., Shanghai, China). The plant β-actin polyclonal antibody was purchased from YIFEIXUE BIO TECH. The proteins used for western blot analysis were extracted from rice seedling tissue with urea extraction buffer, separated on 15% SDS-PAGE gel, then transferred onto a polyvinylidene fluoride membrane (Millipore, USA), which was probed with the anti-A0A0N7KH91 polyclonal antibody and anti-β-actin polyclonal antibody.

Results

Physiological response to cold stress

To validate contrasting stress phenotypes of Kongyu131 and 11 other cultivars in response to cold treatment, two-week-old rice seedlings were treated at 8°Cfor 4 days and then allowed to recover for 3 days. Before cold treatment, seedlings of all varieties grew normally (Fig. 1A). After the cold stress and recovery treatment, seedlings of the indica varieties (Guanghui998, Jinweiai, 02428, Y58S and Dachangli) and several japonica varieties (Liaoxing1, Liaoxing21 and Kunmingxiaobaigu) were completely wilted, whereas most seedlings of japonica rice varieties Kongyu131, Nipponbare and Daohuaxiang were able to survive (Fig. 1B). As seen in Fig. 1C, the survival rate of Kongyu131 after cold stress treatment was 77%, and is the highest survival rate in comparison to other varieties in this experiment.

Fig. 1.

Phenotypes of cold stress treated Kongyu131 and other rice cultivars after 3 days of recovery. Rice seedling were planted in a phytotron under light and temperature controls (the day/night cycle: 12 h with 28°C and 12 h with 22°C). 2-week-old rice seedlings were treated under 8°C for 4 days and then allowed to recover for 3 days under normal temperature. (A) Phenotypes of rice cultivars before low temperature treatment. (B) Phenotypes of rice cultivars after 4 days of low temperature treatment and 3 days of recovery, bar = 2 cm. C, survival rate of rice cultivars after 3 days of recovery.

To identify proteins that respond to cold stress in Kongyu131 at the proteomic level, two-week-old seedlings grown in soil were subjected to 0, 24, 48 and 72 h of cold stress treatments. The shoot tissues of the treated seedlings were used for quantitative proteomic analysis.

Identification and quantitation of proteins with iTRAQ-based LC-MS/MS analysis

MS raw data were analyzed with ProteinPilot (V4.5) to identify and quantify proteins. As shown in Fig. 2A, a total of 89,976 MS/MS spectra were identified by iTRAQ-based LC-MS/MS analysis in time courses of cold stress treated Kongyu131 shoot tissues. Among them, 29,601 peptides were found. At least one unique peptide was identified for each confident protein. A total of 5082 unique proteins were identified by iTRAQ labeling from the time courses of cold-stressed Kongyu131 (Supplemental Table 2).

Fig. 2.

Basic information statistics of the proteome and hierarchical clustering of DEPs identified during cold stress. (A) Basic information statistics of the proteome using iTRAQ analysis. MS/MS spectra are the secondary mass spectrums, and proteins are identified by ProteinPilot (V4.5). (B) Hierarchical clustering of quantified proteins based on LC-MS/MS data.

Through quantitative analysis with the software, 289 unique proteins were differentially expressed with changes greater than 1.2-fold or smaller than 0.83-fold, p-value smaller than 0.05. Regulated proteins formed two major clusters (Fig. 2B). After 24 h of cold treatment, 91 differentially expressed proteins (DEPs) (58 up- and 33 down-regulated) were found (Table 1). As the cold treatment time was increased, the number slightly increased: 179 DEPs (86 up- and 93 down-regulated) at 48 h (Table 2) and 142 DEPs (98 up- and 44 down-regulated) at 72 h (Table 3). Fig. 3 shows the Venn diagram analysis of the DEPs at different time points. Overall, there were 289 unique DEPs (169 uniquely up-regulated and 125 uniquely down-regulated) during cold stress. Among the 169 up-regulated proteins, 11 proteins were found to be significantly up-regulated at three time points (Fig. 3A), and 11 proteins of the 125 down-regulated proteins were found to be significantly down-regulated at three time points (Fig. 3B). Fifty-one proteins were found to be significantly up-regulated and 23 proteins were found to be significantly down-regulated at any two time points, respectively. Five proteins were found to be both significantly up- and down-regulated during cold stress treatments: 4 proteins up-regulated after 24 h of cold stress but down-regulated at 48 h time point, and one protein down-regulated after 48 h of cold stress but up-regulated at the 72 h time point.

Table 1.List of differentially expressed proteins after cold stress treatment for 24 h
Uniprot_IDGene IDPutative functionPeptides (95%)aCov (95%)bFold change
Q06967LOC_Os03g5029014-3-3-like protein GF14-F2874.621.31
Q6ER94LOC_Os02g334502-Cys peroxiredoxin BAS1, chloroplastic1749.811.56
Q10N97LOC_Os03g1696033 kDa secretory protein, putative, expressed635.850.41
O65037LOC_Os01g6995050S ribosomal protein L27, chloroplastic422.051.57
Q7XR19LOC_Os04g39700.160S ribosomal protein L61354.051.54
P0DKK7LOC_Os09g3297660S ribosomal protein L7a-21442.251.38
Q5N6W3LOC_Os01g67720.1ABC1-like822.590.42
Q53JF7LOC_Os11g06720Abscisic stress-ripening protein 5416.670.41
Q9FXT4LOC_Os10g35110Alpha-galactosidase1124.660.62
Q10A56LOC_Os10g05069Alpha-mannosidase2022.710.62
Q2QX58LOC_Os12g07110AMP-binding enzyme family protein, expressed1927.790.60
Q5WAB3LOC_Os06g07090.1AP-1 complex subunit gamma811.030.66
Q84PA4LOC_Os03g17070ATP synthase B chain, chloroplast, putative, expressed2145.971.66
Q7G3Y4LOC_Os10g17280ATP synthase gamma chain, mitochondrial, putative, expressed1243.211.58
Q7XXS0LOC_Os08g37320.1ATP synthase subunit d, mitochondrial1967.461.42
Q7F354LOC_Os01g51570.1Beta-1,3-glucanase730.180.46
Q10RP0LOC_Os03g05730Cell division cycle protein 48, putative, expressed3953.401.21
Q8RU06LOC_Os10g22520Cellulase containing protein, expressed1020.390.55
Q53N83LOC_Os11g13890Chlorophyll a-b binding protein, chloroplastic2251.591.31
Q84PB4LOC_Os08g44680.1Chloroplast photosystem I reaction center subunit II-like protein2570.441.77
Q69S39LOC_Os07g37030Cytochrome b6-f complex iron-sulfur subunit, chloroplastic1246.221.79
Q5WMX0LOC_Os05g15770.1DIP31636.361.75
Q6L4S0LOC_Os05g51480DNA damage-binding protein 11413.580.64
P29545LOC_Os07g46750Elongation factor 1-beta1057.141.41
Q851Y8LOC_Os03g63410Elongation factor Tu2048.121.64
Q2QN11LOC_Os12g39360Eukaryotic aspartyl protease family protein, expressed1025.340.44
Q2R8Z8LOC_Os11g10470Expressed protein113.865.01
Q0J8M2LOC_Os08g01380Ferredoxin-1, chloroplastic440.290.30
P41344LOC_Os06g01850Ferredoxin—NADP reductase, leaf isozyme 1, chloroplastic3056.081.58
Q6ZFJ3LOC_Os02g01340Ferredoxin—NADP reductase, leaf isozyme 2, chloroplastic2954.371.71
Q40677LOC_Os11g07020Fructose-bisphosphate aldolase, chloroplastic4757.731.50
Q10CU9LOC_Os03g53800Glycosyl hydrolase family 3 N terminal domain containing protein, expressed1728.320.60
Q10CU4LOC_Os03g53860Glycosyl hydrolase family 3 N terminal domain containing protein, expressed1923.960.52
Q84TA3LOC_Os03g60460Leucine aminopeptidase1525.200.64
Q8GS76LOC_Os07g44780.1Lipase-like protein13.840.65
Q0D5P8LOC_Os07g36080Oxygen-evolving enhancer protein 3, chloroplastic2258.062.33
Q7F1U0LOC_Os07g48020.1Peroxidase1751.740.50
Q9FYP0LOC_Os01g19020.1Peroxidase1031.990.47
Q6AVZ8LOC_Os05g04380.1Peroxidase1649.120.42
Q0DCP0LOC_Os06g20150.1Peroxidase (Fragment)1439.530.53
Q10CE4LOC_Os03g57220Peroxisomal (S)-2-hydroxy-acid oxidase GLO14169.651.56
Q6YT73LOC_Os07g05820Peroxisomal (S)-2-hydroxy-acid oxidase GLO53865.851.84
A0A0P0WP33LOC_Os05g41640.1Phosphoglycerate kinase4168.182.42
Q8LMR0LOC_Os03g06200Phosphoserine aminotransferase1026.761.72
Q8GT95LOC_Os07g38130Polygalacturonase inhibitor 1832.830.54
Q6AT26LOC_Os05g08370Probable cellulose synthase A catalytic subunit 1 [UDP-forming]55.301.39
Q53LQ0LOC_Os11g09280Protein disulfide isomerase-like 1-12641.801.46
Q60E59LOC_Os05g32220.1Ribosomal protein1739.831.45
A3BLC3LOC_Os07g38300Ribosome-recycling factor, chloroplastic2357.141.49
Q10M12LOC_Os03g21040Ricin B-like lectin R40C11553.451.32
Q0JPA6LOC_Os01g13210Salt stress root protein RS11766.181.79
Q9S827LOC_Os08g02640Succinate dehydrogenase [ubiquinone] iron-sulfur subunit 1, mitochondrial628.472.65
Q0D840LOC_Os07g08840Thioredoxin H1954.921.67
Q6ATY4LOC_Os05g33280UPF0603 protein Os05g0401100, chloroplastic1536.121.45
A0A0N7KH91LOC_Os03g22490.1Os03g0345700 protein (Fragment)16.670.32
Q0DJB9LOC_Os05g23740.1Os05g0303000 protein (Fragment)3346.191.47
Q0DEV8LOC_Os06g04150.1Os06g0132400 protein (Fragment)743.121.64
A0A0P0X3W0LOC_Os07g13280.1Os07g0237100 protein (Fragment)513.531.82
Q0JAK9LOC_Os04g50204.1OSJNBa0009P12.17 protein1113.911.71
Q7XPV4 LOC_Os04g58710.1 OSJNBa0088H09.2 protein 15 38.22 0.46
Q0JN91 LOC_Os01g21250.1 Os01g0314800 protein 1 8.60 5.92
Q943W1 LOC_Os01g31690.1 Os01g0501800 protein 36 66.07 2.07
Q5VPC8 LOC_Os01g46510.1 Os01g0653800 protein 8 8.50 0.62
Q8W0E7 LOC_Os01g52230.1 Os01g0720400 protein 1 3.65 5.20
Q5JKX9 LOC_Os01g72049.1 Os01g0949060 protein 3 8.61 0.62
Q6YU90 LOC_Os02g01150.1 Os02g0101500 protein 42 81.61 1.61
Q6Z702 LOC_Os02g03260.1 Os02g0125100 protein 19 46.30 1.47
A0A0P0VH79 LOC_Os02g15750.1 Os02g0257300 protein 10 17.20 1.92
Q6K4S7 LOC_Os02g18410.1 Os02g0285300 protein 12 41.81 1.67
Q6Z875 LOC_Os02g21970.1 Os02g0325100 protein 12 29.12 1.61
A0A0P0VJP6 LOC_Os02g31160.1 Os02g0516800 protein 2 4.04 0.64
Q69S79 LOC_Os02g36570.1 Os02g0575500 protein 3 4.72 0.56
Q0E032 LOC_Os02g37060.1 Os02g0581100 protein 3 10.92 0.51
Q6ZGJ8 LOC_Os02g52940.1 Os02g0768600 protein 22 64.69 1.45
Q6KA00 LOC_Os02g57670.1 Os02g0822600 protein 15 55.08 1.29
B9F813 LOC_Os04g16680.1 Os04g0234600 protein 38 57.91 1.85
Q0JD53 LOC_Os04g35140.1 Os04g0430700 protein 3 4.89 0.29
Q0JAF4 LOC_Os04g51300.1 Os04g0602100 protein 19 43.63 1.98
A0A0P0WG85 LOC_Os04g56740.1 Os04g0663100 protein 15 16.69 1.63
A0A0P0WKD6 LOC_Os05g22614.1 Os05g0291700 protein 41 69.29 1.74
Q60EA3 LOC_Os05g42350.1 Os05g0503300 protein 20 29.49 1.38
Q658I3 LOC_Os06g03770.1 Os06g0128300 protein 5 6.41 0.52
Q67W57 LOC_Os06g43850.1 Os06g0646500 protein 17 56.00 1.82
A0A0P0X7J0 LOC_Os07g35520.1 Os07g0539400 protein 6 11.32 0.48
Q0D5S1 LOC_Os07g35560.1 Os07g0539900 protein 14 28.24 0.46
Q6ZG03 LOC_Os08g17390.1 Os08g0276100 protein 12 36.03 1.42
Q6YW78 LOC_Os08g29370.1 Os08g0382400 protein 26 47.42 1.77
Q8LNF2 LOC_Os10g35810 Os10g0502000 protein 12 45.34 1.37
A0A0P0Y5F2 LOC_Os11g46000.1 Os11g0687200 protein 8 12.37 1.34
Q2QWN3 LOC_Os12g08770 Os12g0189400 protein 13 53.02 0.67
Q2QSR7 LOC_Os12g23180 Os12g0420200 protein 32 55.05 1.29

a Peptides (95%) indicate the identified peptides having at least 95% confidence.

b Cov (95%) indicate percentage of matching amino acids from identified peptides having at least 95% confidence.

Table 2.List of differentially expressed proteins after cold stress treatment for 48 h
Uniprot_IDPutative functionPeptides (95%)aCov (95%)bFold change
Q8S6N5Acetyl-CoA carboxylase 13215.261.47
Q0J709ACT domain-containing protein DS12, chloroplastic1144.172.03
Q10P83Acyl-CoA-binding domain-containing protein 514.391.63
Q7F270ADP-ribosylation factor 1 OS = Oryza sativa subsp. japonica OX = 39947 GN = OJ1118_B03.103 PE = 2 SV = 1727.072.19
Q7XYS3Allene oxide synthase 21229.081.72
Q9FXT4Alpha-galactosidase1124.660.37
Q10A56Alpha-mannosidase2022.710.40
Q2R3E0Alpha-mannosidase3233.920.34
Q2QX58AMP-binding enzyme family protein, expressed1927.790.61
Q5WAB3AP-1 complex subunit gamma811.030.75
P12085ATP synthase subunit beta, chloroplastic5775.102.47
Q655S1ATP-dependent zinc metalloprotease FTSH 2, chloroplastic3547.342.23
Q2QZU5Auxin-repressed protein-like protein ARP1, putative, expressed345.600.19
Q75I93Beta-glucosidase 71224.800.48
Q0JR25Bowman-Birk type bran trypsin inhibitor833.860.19
A5HEI2Bowman-Birk type proteinase inhibitor A1543.820.19
Q5VS79Calmodulin-binding protein-like1733.890.59
B9EXM2Carbamoyl-phosphate synthase large chain, chloroplastic2926.370.60
Q75HY2Carboxypeptidase1131.451.46
Q10RP0Cell division cycle protein 48, putative, expressed3953.401.24
Q8RU06Cellulase containing protein, expressed1020.390.49
Q84T92Chalcone—flavonone isomerase1153.652.61
Q6H795Chaperone protein ClpD1, chloroplastic1114.291.96
Q6ZF30Chlorophyll a-b binding protein, chloroplastic1051.134.53
Q7XV11Chlorophyll a-b binding protein, chloroplastic1548.412.29
Q53N83Chlorophyll a-b binding protein, chloroplastic2251.591.87
Q6H748Chlorophyll a-b binding protein, chloroplastic1631.971.87
Q7XC09Chloroplast chaperonin 10, putative, expressed854.290.60
Q84PB4Chloroplast photosystem I reaction center subunit II-like protein2570.442.11
Q5W6F1Cinnamate-4-hydroxylase815.002.11
Q6YUR8Cold shock domain protein 11168.880.50
Q7XCS3Cys/Met metabolism PLP-dependent enzyme family protein, expressed511.422.61
P12123Cytochrome b6525.123.66
Q6ZAA5D-3-phosphoglycerate dehydrogenase1017.231.80
Q5WMX0DIP31636.360.63
Q306J3Dirigent protein1658.502.88
Q69JX7Drought-induced S-like ribonuclease313.100.42
Q8S3P3DUF26-like protein1044.960.29
O64937Elongation factor 1-alpha2948.770.23
Q5Z627Elongation factor 1-gamma 32143.510.50
Q6ZI53Elongation factor Tu4161.880.43
Q2QN11Eukaryotic aspartyl protease family protein, expressed1025.340.37
Q8S7Q0Eukaryotic translation initiation factor 3 subunit B1724.760.63
Q0DHB7Expansin-A413.660.27
Q2R8Z8Expressed protein113.868.63
Q10T66Expressed protein949.750.68
Q7G649Expressed protein1561.820.36
P41344Ferredoxin—NADP reductase, leaf isozyme 1, chloroplastic3056.081.60
Q6ZFJ3Ferredoxin—NADP reductase, leaf isozyme 2, chloroplastic2954.372.81
Q6ZD89Flavone 3ʹ-O-methyltransferase 12060.601.82
Q5N725Fructose-bisphosphate aldolase 3, cytoplasmic2872.070.74
Q5VQG4Galactinol—sucrose galactosyltransferase68.942.01
Q6Z2T6Geranylgeranyl diphosphate reductase, chloroplastic2647.080.67
Q6ZBZ2Germin-like protein 8-14415.960.42
Q10CU9Glycosyl hydrolase family 3 N terminal domain containing protein, expressed1728.320.58
Q10CU4Glycosyl hydrolase family 3 N terminal domain containing protein, expressed1923.960.51
Q7XU02Glycosyltransferase37.342.17
Q5VME5Glycosyltransferase1131.192.00
Q2R1S1Harpin binding protein 1, putative, expressed931.111.63
Q7XUC9Histone H41258.251.89
O64437Inositol-3-phosphate synthase 148.432.27
Q84TA3Leucine aminopeptidase1525.200.64
Q03200Light-regulated protein, chloroplastic336.720.18
P29250Linoleate 9S-lipoxygenase 21723.791.74
Q8GS76Lipase-like protein13.840.77
Q2QNN5Lipoxygenase2328.747.80
Q7XUG1Malate synthase1429.810.47
Q75M18Methionine S-methyltransferase1112.181.64
Q2QM23Methyl-CpG binding domain containing protein, expressed1356.440.39
Q7XUK3NADPH oxidoreductase1036.233.02
Q2QYL3Non-specific lipid-transfer protein 3748.760.16
Q75M32Peptidyl-prolyl cis-trans isomerase2360.400.50
Q6ZH98Peptidyl-prolyl cis-trans isomerase1655.230.50
Q5Z9H9Peptidyl-prolyl cis-trans isomerase1755.910.39
Q7F1F2Peptidylprolyl isomerase2236.380.72
Q7XSV2Peroxidase1851.160.67
Q0IMX5Peroxidase926.800.61
Q7XSU8Peroxidase1248.530.55
Q6AVZ8Peroxidase1649.120.49
Q7XSU7Peroxidase1435.510.47
Q5Z7J2Peroxidase1330.860.30
A0A0P0XR31Peroxidase (Fragment)1232.140.55
Q0DCP0Peroxidase (Fragment)1439.530.40
Q6YT73Peroxisomal (S)-2-hydroxy-acid oxidase GLO53865.851.82
Q6K6Q1Phenylalanine ammonia-lyase2330.084.02
P14717Phenylalanine ammonia-lyase4455.352.88
Q75W16Phospho-2-dehydro-3-deoxyheptonate aldolase 2, chloroplastic1425.602.42
A0A0P0WP33Phosphoglycerate kinase4168.182.27
Q6Z8F4Phosphoribulokinase2757.321.53
Q8LMR0Phosphoserine aminotransferase1026.761.77
P0C355Photosystem I P700 chlorophyll a apoprotein A11916.801.51
P0C364Photosystem II CP47 reaction center protein2431.892.54
P0C434Photosystem II protein D11831.732.99
Q8L6I2Plasma membrane ATPase2021.321.58
Q8GT95Polygalacturonase inhibitor 1832.830.32
Q6ZC69Probable adenylate kinase 2, chloroplastic933.451.80
Q5ZCK5Probable calcium-binding protein CML16531.492.58
Q75L11Probable histone H2A.6630.134.37
Q53RB0Probable linoleate 9S-lipoxygenase 41416.421.84
Q6K439Probable plastid-lipid-associated protein 2, chloroplastic1742.011.66
Q5VMJ3Profilin LP04658.780.70
Q7XKF3Protochlorophyllide reductase A, chloroplastic1748.060.31
Q6YZX6Putative aconitate hydratase, cytoplasmic3941.980.68
A0A0P0WK98Ribosomal protein L15 (Fragment)933.820.59
A3BLC3Ribosome-recycling factor, chloroplastic2357.140.59
Q10M12Ricin B-like lectin R40C11553.450.55
P31924Sucrose synthase 16354.900.59
Q7XXS4Thiamine thiazole synthase, chloroplastic2342.821.61
Q0D840Thioredoxin H1954.920.49
Q6ZFU6Thioredoxin reductase NTRB741.091.75
Q5N9C8Trafficking protein particle complex subunit217.532.11
P1214930S ribosomal protein S12, chloroplastic212.900.54
P0C48530S ribosomal protein S3, chloroplastic1032.640.58
Q10N9833 kDa secretory protein, putative, expressed632.450.10
Q0IQF740S ribosomal protein S161047.650.60
Q8L4F240S ribosomal protein S23, putative, expressed548.590.54
Q8LI304-alpha-glucanotransferase DPE1, chloroplastic/amyloplastic610.942.17
Q2QU0660 kDa chaperonin alpha subunit4057.960.61
Q2QNF360S ribosomal protein L2, putative, expressed1745.980.61
P3568460S ribosomal protein L32136.500.63
A0A0N7KH91Os03g0345700 protein (Fragment)16.670.10
A0A0P0VRK8Os02g0818000 protein (Fragment)628.700.23
A0A0P0VUM8 Os03g0213100 protein (Fragment) 6 10.11 2.21
Q0DSD6Os03g0315800 protein (Fragment)2237.690.65
A0A0P0WKQ1Os05g0323800 protein (Fragment)920.461.51
A0A0P0WTX9Os06g0214850 protein (Fragment)1038.921.75
A0A0P0X3W0Os07g0237100 protein (Fragment)513.531.84
C7JA48Os12g0478100 protein (Fragment)218.495.30
Q7XVP0OSJNBa0023J03.8 protein421.453.77
Q7X7H3OSJNBa0076N16.12 protein1729.041.64
Q7XW32OSJNBb0062H02.10 protein1656.940.48
Q7X6F6OSJNBb0079B02.3 protein1825.230.42
Q0JR27Os01g0124100 protein524.320.12
Q0JQZ2Os01g0130400 protein44.720.70
Q9SDK4Os01g0254000 protein635.232.03
Q0JN91Os01g0314800 protein18.6012.71
Q943W1Os01g0501800 protein3666.071.82
Q5VP66Os01g0644000 protein322.310.14
Q5N754Os01g0815800 protein825.930.59
Q943L0Os01g0839900 protein626.320.41
Q943K1Os01g0869800 protein1437.312.78
Q6YU90Os02g0101500 protein4281.611.27
Q6EUK5Os02g0234500 protein1735.431.96
Q6ZH84Os02g0593700 protein34.732.78
Q6K9C2Os02g0610700 protein517.951.84
Q6K1Q6Os02g0622300 protein1141.790.72
Q6Z8I7Os02g0752200 protein1219.360.61
Q6K3F7Os02g0812400 protein34.512.42
Q10N92Os03g0278200 protein59.782.25
A0A0N7KH54Os03g0311300 protein627.451.79
Q10K10Os03g0401100 protein11.730.59
Q94H99Os03g0761000 protein532.640.37
A0A0P0W5A9Os03g0841900 protein46.981.33
Q84M68Os03g0856500 protein924.600.26
B9F813Os04g0234600 protein3857.911.77
Q7XIK5Os04g0613600 protein946.580.60
B9FM04Os05g0104650 protein1310.740.64
Q0DK70Os05g0188100 protein442.700.33
Q75IK4Os05g0209600 protein823.820.34
A0A0P0WKD6Os05g0291700 protein4169.291.80
Q0DG76Os05g0549100 protein1833.450.47
Q0DG31Os05g0556100 protein1828.411.54
Q0DFD6Os05g0597100 protein517.170.59
Q9LWT6Os06g0114000 protein5767.890.44
Q0DEF1Os06g0157000 protein932.840.32
Q8GTK4Os07g0141400 protein2560.631.67
Q84PB5Os07g0148900 protein622.564.02
Q6ZLQ0Os07g0150100 protein89.441.26
Q6ZLB8Os07g0180900 protein3160.000.66
Q0D5S1Os07g0539900 protein1428.240.29
Q6YPF2Os08g0120500 protein2334.080.52
Q7EYM8Os08g0379400 protein2456.412.51
Q6Z8N9Os08g0512400 protein823.730.44
A0A0P0XX30Os10g0530500 protein1056.001.80
Q2QZH3Os11g0687100 protein1216.432.15
A0A0P0Y5F2Os11g0687200 protein812.371.82
Q0IPL3Os12g0189300 protein1550.003.37
Q2QWN3Os12g0189400 protein1353.020.72
Q2QNS7Os12g0555500 protein1070.252.58

a Peptides (95%) indicate the identified peptides having at least 95% confidence.

b Cov (95%) indicate percentage of matching amino acids from identified peptides having at least 95% confidence.

Table 3.List of differentially expressed proteins after cold stress treatment for 72 h
Uniprot_IDPutative functionPeptides (95%)aCov (95%)bFold change
Q0J709ACT domain-containing protein DS12, chloroplastic1144.172.03
Q10P83Acyl-CoA-binding domain-containing protein 514.391.94
A0A0P0Y1Y5Adenosylhomocysteinase (Fragment)3055.151.27
Q7XYS3Allene oxide synthase 21229.081.72
Q9FXT4Alpha-galactosidase1124.660.43
Q10A56Alpha-mannosidase2022.710.67
Q5WAB3AP-1 complex subunit gamma811.030.72
P0C2Z6ATP synthase subunit alpha, chloroplastic3651.681.20
P0C522ATP synthase subunit alpha, mitochondrial3153.241.71
Q93VT8ATP-citrate synthase beta chain protein 12546.711.43
Q5Z974ATP-dependent zinc metalloprotease FTSH 1, chloroplastic3440.381.32
Q655S1ATP-dependent zinc metalloprotease FTSH 2, chloroplastic3547.341.58
Q0JR25Bowman-Birk type bran trypsin inhibitor833.860.39
Q2QMX9Calcium-transporting ATPase 10, plasma membrane-type76.761.43
B9EXM2Carbamoyl-phosphate synthase large chain, chloroplastic2926.370.65
Q75HY2Carboxypeptidase1131.451.56
Q10RP0Cell division cycle protein 48, putative, expressed3953.401.79
Q84T92Chalcone—flavonone isomerase1153.652.51
Q6H795Chaperone protein ClpD1, chloroplastic1114.293.70
Q6H748Chlorophyll a-b binding protein, chloroplastic1631.972.00
Q7XV11Chlorophyll a-b binding protein, chloroplastic1548.412.13
Q53N83Chlorophyll a-b binding protein, chloroplastic2251.591.38
Q6ZF30Chlorophyll a-b binding protein, chloroplastic1051.133.08
Q10HD0Chlorophyll a-b binding protein, chloroplastic1645.252.73
Q5W6F1Cinnamate-4-hydroxylase815.002.05
Q6ZGV8Clustered mitochondria protein homolog87.361.53
Q6YUR8Cold shock domain protein 11168.880.48
Q7XCS3Cys/Met metabolism PLP-dependent enzyme family protein, expressed511.423.16
Q7XKC8Dihydroorotate dehydrogenase (quinone), mitochondrial513.220.25
Q306J3Dirigent protein1658.501.82
Q69JX7Drought-induced S-like ribonuclease313.100.49
Q2QN11Eukaryotic aspartyl protease family protein, expressed1025.340.43
Q7G649Expressed protein1561.820.52
Q2R678Expressed protein1127.671.54
Q10T66Expressed protein949.750.51
Q2R8Z8Expressed protein113.8614.32
P41344Ferredoxin—NADP reductase, leaf isozyme 1, chloroplastic3056.081.77
Q6ZFJ3Ferredoxin—NADP reductase, leaf isozyme 2, chloroplastic2954.372.23
Q6ZD89Flavone 3ʹ-O-methyltransferase 12060.601.96
Q69V57Fructose-bisphosphate aldolase3175.141.42
Q40677Fructose-bisphosphate aldolase, chloroplastic4757.731.47
Q5VQG4Galactinol—sucrose galactosyltransferase OS = Oryza sativa subsp. japonica OX = 39947 GN = RFS PE = 1 SV = 168.942.49
Q6Z2T6Geranylgeranyl diphosphate reductase, chloroplastic2647.080.64
P15280Glucose-1-phosphate adenylyltransferase small subunit 2, chloroplastic/amyloplastic/cytosolic2241.051.33
P14656Glutamine synthetase cytosolic isozyme 1-1933.991.74
Q945W2Glutathione S-transferase GSTU6, putative, expressed628.812.15
Q8H8D6Glutathione S-transferase, N-terminal domain containing protein, expressed1646.390.62
Q0J8A4Glyceraldehyde-3-phosphate dehydrogenase 1, cytosolic2862.022.81
A3C6G9Glycine cleavage system H protein, mitochondrial867.680.41
Q10CU9Glycosyl hydrolase family 3 N terminal domain containing protein, expressed1728.320.60
Q7XU02Glycosyltransferase37.342.65
Q5VME5Glycosyltransferase1131.191.82
Q7XUC9Histone H41258.252.96
Q851P9Histone-like protein619.113.73
O64437Inositol-3-phosphate synthase 148.432.40
Q84TA3Leucine aminopeptidase1525.200.65
Q6K669Leucine aminopeptidase 2, chloroplastic2739.631.29
P38419Lipoxygenase 7, chloroplastic1618.941.54
Q7XZW5Malate dehydrogenase3485.311.64
Q2QM23Methyl-CpG binding domain containing protein, expressed1356.440.52
Q7XUK3NADPH oxidoreductase1036.233.70
Q0D5P8Oxygen-evolving enhancer protein 3, chloroplastic2258.061.25
Q5JMS4Peroxidase1850.401.79
Q6AVZ8Peroxidase1649.120.44
Q9FYP0Peroxidase1031.990.48
Q0JM38Peroxidase825.590.27
Q5Z7J2Peroxidase1330.860.47
Q0DCP0Peroxidase (Fragment)1439.530.50
A0A0P0XR31Peroxidase (Fragment)1232.140.57
Q6YT73Peroxisomal (S)-2-hydroxy-acid oxidase GLO53865.852.01
P14717Phenylalanine ammonia-lyase4455.352.70
Q6K6Q1Phenylalanine ammonia-lyase2330.083.98
Q0DZE0Phenylalanine ammonia-lyase2227.915.86
Q75W16Phospho-2-dehydro-3-deoxyheptonate aldolase 2, chloroplastic1425.602.49
A0A0P0WP33Phosphoglycerate kinase4168.181.92
P0C355Photosystem I P700 chlorophyll a apoprotein A11916.801.56
P0C358Photosystem I P700 chlorophyll a apoprotein A22020.842.56
P0C364Photosystem II CP47 reaction center protein2431.892.75
Q8GT95Polygalacturonase inhibitor 1832.830.43
Q5ZCK5Probable calcium-binding protein CML16531.492.05
Q75L11Probable histone H2A.6630.134.41
Q6K439Probable plastid-lipid-associated protein 2, chloroplastic1742.011.79
Q0D5W6Protein translation factor SUI1 homolog643.482.68
Q6ZHE5Putative D-cysteine desulfhydrase 1, mitochondrial1334.981.34
Q7XKB5Pyruvate kinase1832.091.22
P31924Sucrose synthase 16354.901.33
Q93X08UTP—glucose-1-phosphate uridylyltransferase3560.551.39
Q0696714-3-3-like protein GF14-F2874.621.43
Q6ER942-Cys peroxiredoxin BAS1, chloroplastic1749.811.45
P0C48830S ribosomal protein S4, chloroplastic1142.292.03
Q8LI304-alpha-glucanotransferase DPE1, chloroplastic/amyloplastic610.942.09
Q2QLY55-methyltetrahydropteroyltriglutamate—homocysteine methyltransferase 14043.992.03
P4921060S ribosomal protein L91155.261.54
Q2R4806-phosphogluconate dehydrogenase, decarboxylating 2, chloroplastic1737.201.43
A0A0N7KH91Os03g0345700 protein (Fragment)16.670.12
A0A0P0VRK8Os02g0818000 protein (Fragment)628.700.29
A0A0P0VTG7Os03g0165300 protein (Fragment)48.650.18
A0A0P0VUM8Os03g0213100 protein (Fragment)610.112.38
Q0DEU8Os06g0133800 protein (Fragment)5264.451.50
A0A0P0Y445Os11g0602500 protein (Fragment)1337.321.71
C7JA48Os12g0478100 protein (Fragment)218.4911.91
Q7XPV4OSJNBa0088H09.2 protein1538.220.68
Q7XVF8OSJNBb0118P14.7 protein515.382.00
Q94EG1Os01g0178000 protein724.370.69
Q5VR43Os01g0180300 protein5155.480.61
Q9SDK4Os01g0254000 protein635.231.57
Q9FP25Os01g0303000 protein1462.900.44
Q0JN91Os01g0314800 protein18.608.32
Q6YU90Os02g0101500 protein4281.611.58
Q6YUV4Os02g0189000 protein632.880.51
Q6EUK5Os02g0234500 protein1735.431.67
Q6ZH84Os02g0593700 protein34.734.83
Q6Z8I7Os02g0752200 protein1219.360.62
Q0DWG3Os02g0816300 protein43.930.40
Q10RI4Os03g0158500 protein58.772.17
Q10N92Os03g0278200 protein59.782.25
Q9AUQ4Os03g0712700 protein3251.031.42
Q8S7H8Os03g0778100 protein1137.292.29
B9F813Os04g0234600 protein3857.911.89
Q0JAF4Os04g0602100 protein1943.631.77
Q75IR7Os05g0163000 protein1526.731.46
Q0DK70Os05g0188100 protein442.700.42
Q75IK4 Os05g0209600 protein 8 23.82 0.57
Q6F322 Os05g0490700 protein 9 13.84 1.33
Q0DG76 Os05g0549100 protein 18 33.45 0.51
Q9SNN5 Os06g0130800 protein 8 30.83 0.69
Q7XXQ8 Os06g0232000 protein 6 16.80 0.36
Q69WH2 Os06g0332800 protein 6 44.27 0.43
Q93W07 Os06g0568200 protein 23 50.82 1.24
Q67W57 Os06g0646500 protein 17 56.00 2.03
Q5YM05 Os06g0720400 protein 10 22.75 1.58
Q8GTK4 Os07g0141400 protein 25 60.63 1.57
Q84PB5 Os07g0148900 protein 6 22.56 4.66
A0A0P0X7J0 Os07g0539400 protein 6 11.32 0.40
Q0D5S1 Os07g0539900 protein 14 28.24 0.63
Q0D3Z0 Os07g0658300 protein 1 1.03 0.72
Q6ZFI6 Os08g0502700 protein 29 58.21 1.82
Q650W6 Os09g0565200 protein 13 36.65 0.54
Q2QZH3 Os11g0687100 protein 12 16.43 1.67
A0A0P0Y5F2 Os11g0687200 protein 8 12.37 1.72
Q0IPL3 Os12g0189300 protein 15 50.00 2.51
Q2QSR7 Os12g0420200 protein 32 55.05 1.33

a Peptides (95%) indicate the identified peptides having at least 95% confidence.

b Cov (95%) indicate percentage of matching amino acids from identified peptides having at least 95% confidence.

Fig. 3.

Venn diagram analysis of the differentially expressed proteins in rice at each time point of cold stress treatment. The numbers of the differentially expressed proteins identified after 24 h, 48 h and 72 h of cold treatment are shown in the different segments. (A) The up-regulated proteins. (B) The down-regulated proteins.

GO analysis of DEPs

To further understand the functions of DEPs, GO analysis was performed. 253 protein IDs of 289 unique DEPs were assigned functions in the GO analysis. The DEPs were significantly enriched in 13/14/11 biological processes at 24/48/72 h cold stress treatment, 10/10/8 cellular components at 24/48/72 h cold stress treatment, and 7/8/6 molecular function subgroups at 24/48/72 h cold stress treatment. The metabolic process, cellular process and response to stimulus groups were prominent in the biological process subgroup, indicating that the metabolic processes are more quickly affected under cold stress (Fig. 4A). The cell part, organelle, organelle part, membrane part and protein-containing complex groups were highly localized within the cellular component subgroup (Fig. 4B). Among the DEPs, the enriched GO terms concerning molecular function showed that DEPs were mainly associated with catalytic activity and binding, followed by the structural molecule activity, antioxidant activity and molecular function regulator (Fig. 4C).

Fig. 4.

Bioinformatics analysis of DEPs in rice after 24 h, 48 h and 72 h of cold stress treatments (ratio >1.2 or <0.83). (A) Biological process. (B) Cellular component. (C) Molecular function.

Pathway enrichment analysis of DEPs

DEPs of 24 h, 48 h and 72 h cold treatment were mapped to the reference pathway in the KEGG database for functional analysis. The metabolic pathways, photosynthesis, phenylpropanoid biosynthesis, carbon metabolism and carbon fixation in photosynthetic organisms were significantly enriched in three time points of the cold stress treatment (Fig. 5). There were some different pathways enriched in different cold tress time points. For example, oxidative phosphorylation, glucosinolate biosynthesis, vitamin B6 metabolism were specifically enriched at 24 h cold stress treatment (Fig. 5A); linoleic acid metabolism, cyanoamino metabolism, thiamine metabolism and zeatin biosynthesis were specifically enriched at 48 h cold stress treatment (Fig. 5B); cysteine and methionine metabolism, galactose metabolism pentose phosphate pathway and starch/sucrose metabolism were specifically enriched at 72 h cold stress treatment (Fig. 5C). As the cold stress time increased, enriched pathways were more and more stable, with ten of the pathways affected in both the 48 h and 72 h cold stress time points (Fig. 5).

Fig. 5.

KEGG pathway analysis of DEPs in rice with cold stress treatments (ratio >1.2 or <0.83). (A) DEPs of 24 h cold treatment. (B) DEPs of 48 h cold treatment. (C) DEPs of 72 h cold treatment.

Protein-protein interaction analysis of DEPs

The protein-protein interaction networks of DEPs from three time points of cold stress treatment containing biological processes, cellular components and molecular function were constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins 11.0 (STRING 11.0) database. By removing unconnected proteins, the resulting network of 24 h cold response proteins contained 64 protein nodes and 360 edges (Fig. 6A), the resulting network of 48 h cold response proteins contained 121 protein nodes and 546 edges (Fig. 6B), and the resulting network of 72 h cold response proteins contained 90 protein nodes and 402 edges (Fig. 6C). In biological processes, DEPs that function in metabolic process, cellular process, response to stimulus and biological regulation are highly up-regulated during cold stress; in terms of cellular components, DEPs that function in extracellular region, membrane, protein-containing complex and cell parts are up-regulated during cold stress; in terms of molecular function, DEPs that function in catalytic activity, binding and molecular function regulator are highly up-regulated during cold stress.

Fig. 6.

The protein-protein interaction network of cold-response proteins generated using the STRING database. Medium confidence (STRING score = 0.4) was set in the network analysis. The edges represent predicted protein-protein associations. (A) DEPs of 24 h cold stress treatment. (B) DEPs of 48 h cold stress treatment. (C) DEPs of 72 h cold stress treatment. Lake blue lines represent data from curated databases, pink lines represent data that was experimentally determined, green lines represent gene neighborhood, red lines represent gene fusions, dark blue lines represent gene co-occurrence, yellow lines represent text mining, black lines represent co-expression, sky blue lines represent protein homology.

Validation of iTRAQ data on selected candidates by Q-PCR and western blot

19 up-regulated DEPs and 6 down-regulated DEPs were selected for Q-PCR analysis, to validate the relationship of expression profiles between mRNA and protein level. We compared the transcription levels of 24 h, 48 h and 72 h cold stress treatments with the iTRAQ data. As shown in Fig. 7, Q-PCR data indicated that the mRNA levels of 19 up-regulated DEPs increased under cold stress, the regulation trends of four DEPs (Q7XUK3, Q6ZH84, Q6ZFJ3, A0A0P0WP33) at three time points of cold treatment were consistent with the iTRAQ quantification data. The mRNA levels of four out of six down-regulated DEPs decreased under cold stress, and the regulation trends of two DEPs (A0A0N7KH91 and Q9FXT4) at three time points of cold treatment were consistent with the iTRAQ quantification data (Fig. 7). The relative mRNA levels of some proteins were inconsistent with the iTRAQ data, maybe the expression of these genes is controlled by posttranscriptional regulation processes involving translation initiation, mRNA and protein stability (Tian et al. 2004).

Fig. 7.

Comparative analysis of the protein and mRNA profiles of 25 representative DEPs. The X-axis represents the time points in the cold treatments. The Y-axis indicates the normalized relative protein and mRNA levels. The red and black columns represent the patterns of protein and mRNA expression in Kongyu131, respectively.

To further validate the protein regulation levels of DEPs identified by the iTRAQ labeling analysis, one down-regulated protein A0A0N7KH91 was selected for confirmation by western blot analysis with a specific peptide antibody raised against the protein, and using β-actin antibody as control. Fig. 8 showed that A0A0N7KH91 protein also down-regulated during cold stress treatment from 24 h to 72 h. This result further confirmed the iTRAQ labeling analysis data.

Fig. 8.

Western blot analysis of time courses of cold-treated rice cultivar Kongyu131. Equal amounts of 50 μg of total protein from different samples was used for western blot analysis using the enhanced chemiluminescence (ECL) method. The specific antibodies against A0A0N7KH91 protein (1:500) and β-actin (1:5000) were used to detect the corresponding protein expression.

Discussion

In this work, we performed quantitative proteomic analysis of japonica rice seedlings subjected to time course cold stress treatments to obtain the dynamic proteins expression patterns responsive to low temperature. Using iTRAQ labeling coupled with LC-MS/MS analysis, 5802 proteins were identified and were used for quantification from the rice tissues. As a result, we found 91/179/142 cold responsive proteins at 24/48/72 h cold stress treatments with the fold change >1.2 or <0.83 with a p-value <0.05 for the differentially regulated proteins (Tables 13), and the number of cold responsive proteins increased when the treatment time increased. This result is consistent with a previous quantitative proteomic analysis of indica rice that also used a time course of cold stress treatment (Wang et al. 2018a).

Increasing proteomics studies on rice cold stress treatment are being used to explore cold response proteins for understanding plant cold-tolerance mechanism. Some proteins that were identified previously to be cold response proteins have been further confirmed in our study using the quantitative proteomic method. These proteins mainly include: sucrose synthase (Cui et al. 2005, Maraña et al. 1990), phenylalanine ammonia-lyse (Cui et al. 2005, Leyva et al. 1995, Sanchez-Ballesta et al. 2000), GSTs (Binh and Oono 1992, Cui et al. 2005, Marrs 1996, Roxas et al. 1997), 14-3-3 like protein GF14-F/Drought induced protein 3/Peroxidase/Phosphoserine aminotransferase (Wang et al. 2018a), ATP synthase (Cen et al. 2018, Cui et al. 2005, Ji et al. 2017, Wang et al. 2018a, Yan et al. 2006), cold shock domain protein (Chaikam and Karlson 2008), drought-induced S-like ribonuclease (Neilson et al. 2011), DUF26-like protein/Photosystem related proteins/Non-specific lipid-transfer protein (Cen et al. 2018, Wang et al. 2018a), and malate dehydrogenase (Lee et al. 2009, Yan et al. 2006).

Based on an analysis of the physiological functions of cold responsive proteins in previous studies, some relative to cold genes identified in the present work may serve to resist cold stress. 14-3-3 proteins can regulate target proteins involved in responses to biotic and abiotic stress through protein interactions (Chen et al. 2006, Cooper et al. 2003), and it also may enhance tolerance to abiotic stress by ion channels regulation and hormone signaling pathways participation (Zhao et al. 2021). 14-3-3-like protein GF14c can target to plasma, thylakoid and vacuolar membranes and associated ATPase synthase complexes involved in stress responses (Baunsgaard et al. 1998, Cooper 2001, Sehnke et al. 2000). 14-3-3-like protein GF14-F (Q06967) was shown to be up-regulated during cold stress in this study, and probably functions in cold resistant in the cold tolerance rice cultivar Kongyu131. Calcium-transporting ATPase genes differentially expressed under cold, salt and drought stresses are involved in abiotic stress signaling (Singh et al. 2014), the calcium-transporting ATPase 10 protein (Q2QMX9) up-regulated after 72 h cold stress treatment might trigger a stress signaling pathway. Chaperone protein ClpD1 is involved in heat and osmotic stress response, and up-regulation of this protein is correlated with increased drought tolerance in rice (Wu et al. 2016). Thus, the up-regulation of chaperone protein (Q6H795) both at 48 h and 72 h of cold stress treatment might play a role in resisting cold stress in this experiment. 6-phosphogluconate dehydrogenase activity increased in rice seedlings during various abiotic stresses treatments, and might function as a regulator to control the efficiency of the pathway under abiotic stresses (Hou et al. 2007). Thus, 6-phosphogluconate dehydrogenase (Q2R480) up-regulated maybe regulate the pathway efficiency under cold stress in this experiment. Cold shock domain proteins can have inducible expression under cold stress conditions for cold acclimation, as seen in Arabidopsis and winter wheat (Fusaro et al. 2007, Karlson and Imai 2003, Karlson et al. 2002). Cold shock domain proteins do not accumulated during low temperature stress treatment in rice (Chaikam and Karlson 2008), and in the present study, the cold shock domain protein 1 (Q6YUR8) down-regulated both at 48 h and 72 h cold stress treatments (Tables 13). Thus, the protein might have different regulation in different rice varieties, which have different resistant levels to cold stress. These correlative data support the notion that the protein might involved in the cold acclimation response.

Interesting, in the 289 DEPs, only 11 proteins continued to be up-regulated and 11 proteins continued to be down-regulated from 24 h to 72 h of cold stress treatment. These continuously regulated proteins during cold stress could play and important role for rice cold resistance. Three proteins (cell division cycle protein, GLO1 and GLO5) have been reported relative to cold stress in previous studies. Cell division cycle protein 48 (CDC48) is associated with leaf senescence and plant survival in rice (Huang et al. 2016), and a single base substitution in yeast CDC48 can change yeast sensitivity to cold stress and cause cell death (Madeo et al. 1997). A homolog of AtCDC48, AtOM66 which is located on the outer mitochondrial membrane in Arabidopsis plays a role in regulating cell death in response to biotic and abiotic stresses (Zhang et al. 2014). In the present study, CDC48 protein (Q10RP0) up-regulation may play role in enhancing survival of Kongyu131 by determining the progression of cell death during cold stress treatment. In a previous study, Glycolate oxidase (GLO) potentially interacted with catalase (CAT) to regulate H2O2 levels in rice under environmental stress or stimuli (Zhang et al. 2016). In the present study, GLO1 (Q10CE4) and GLO5 (Q6YT73) proteins up-regulation may affect H2O2 levels in Kongyu131 to enhance cold resistance. Although other DEPs identified in the work were not reported to function in cold stress response, these up- or down-regulated proteins under cold stress can now be annotated as “cold-regulated proteins”. The physiological functions of these DEPs will need to by fully characterized in future studies to enhance our understanding of cold stress responses in plants at the molecular level.

In conclusion, this study is the first to adopt iTRAQ-based quantitative proteomics approach to identify cold response proteins in cold-tolerance japonica rice cultivar Kongyu131. A total of 289 DEPs were identified in time courses cold stress treatment. Partial DEPs related to cold genes were also identified in this study, 14-3-3 proteins, cold shock domain protein, calcium-transporting ATPase, 6-phosphogluconate dehydrogenase, CDC48 protein and GLO1/5. Some unknown function DEPs were first be identified in this study, specially continue up-regulated proteins (Q0JN91, Q6YU90, B9F813, A0A0P0Y5F2) and continue down-regulated proteins (A0A0N7KH91, Q0D5S1) from 24 h to 72 h cold stress treatment may play an important role during cold tolerance of Kongyu131. Some DEPs were not identified in previous studies can provide candidate genes for biological function study to better understand the cold-tolerance mechanism of rice responses to cold stress. Uncover the function of these genes may provide candidate genes for cold-tolerance rice molecular breeding in future.

Author Contribution Statement

D.Q., G.D. (Gaoxing Dai) and G.D. (Guofu Deng) contributed to experimental design. D.Q. and K.L. contributed to the protein extraction, peptide preparation, iTRAQ labeling experiments and analyzed the quantitative proteomics data; Y.P. and L.G. contributed to Q-PCR experiment; Y.P., H.L., W.C., C.L. and J.H. contributed to the rice cold stress treatment experiment; W.Z., J.G., J.L. and H.W. contributed to the western blot experiment; D.Q., G.D. (Guofu Deng) and G.D. (Gaoxing Dai) wrote the manuscript; G.D. (Gaoxing Dai) contributed to modification of the manuscript.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant no. 31960059, 31960401, U20A2032), Guangxi Natural Science Foundation (grant no. 2017GXNSFAA198266, 2018GXNSFAA050128), Guangxi Science and Technology Base and Special Talents (grant no. GuiKe AD18281069, GuiKe AD18050002, GuiKe AD17129064), Science-Technology Development Funding of Guangxi Academy of Agricultural Science (grant no. 31960059, 2021JM23, GuiNongKe2020YM124), Guangxi Talent High Land of High Quality Breeding Research (grant no. Talent High Land QN-27). We thank Dr. Caren Chang (University of Maryland) for proofreading the manuscript.

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