ITRAQ-based quantitative proteomic analysis of japonica rice seedling during cold stress

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 coldtolerant 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 . 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 ; bZIP73 Jap 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 ; rice OsMADS57 interacts with OsTB1 and both directly target OsWRKY94 and D14 for adaptation to cold . 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) ) and 67 QTLs , 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 coldtolerant 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 stressregulated proteins that have not been previously identified.

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 resuspension buffer (100 mM 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.
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 Taq TM (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.

Physiological response to cold stress
To validate contrasting stress phenotypes of Kongyu131 and 11 other cultivars in response to cold treatment, twoweek-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.
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 iTRAQbased 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).
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 downregulated) 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).  regulated) during cold stress. Among the 169 up-regulated proteins, 11 proteins were found to be significantly upregulated 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). Fiftyone proteins were found to be significantly up-regulated and 23 proteins were found to be significantly downregulated 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.

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).

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    (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).

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, proteincontaining 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.

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 Quantitative proteomic analysis of cold-tolerance rice seedling Breeding Science Vol. 72 No. 2 and protein stability (Tian et al. 2004).
To further validate the protein regulation levels of DEPs identified by the iTRAQ labeling analysis, one downregulated 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.

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 1-3), 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), droughtinduced S-like ribonuclease (Neilson et al. 2011), DUF26like protein/Photosystem related proteins/Non-specific lipidtransfer 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 Quantitative proteomic analysis of cold-tolerance rice seedling Breeding Science Vol. 72 No. 2 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 . 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. 6phosphogluconate 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, 6phosphogluconate 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 1-3). 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 downregulated 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. 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. 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 determin-ing the progression of cell death during cold stress treatment. In a previous study, Glycolate oxidase (GLO) potentially interacted with catalase (CAT) to regulate H 2 O 2 levels in rice under environmental stress or stimuli ). In the present study, GLO1 (Q10CE4) and GLO5 (Q6YT73) proteins up-regulation may affect H 2 O 2 levels in Kongyu131 to enhance cold resistance. Although other DEPs identified in the work were not reported to Quantitative proteomic analysis of cold-tolerance rice seedling Breeding Science Vol. 72 No. 2 function in cold stress response, these up-or downregulated 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 iTRAQbased 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.   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.