Proteomic Analysis of Grain Protein in the Winter Wheat Jimai44 and Jimai229 Based on Strong Gluten Characteristics

Background(cid:0)To determine the grain proteome of the strong gluten wheat, TMT technology was used to analyze proteomic differences between the winter wheat varieties Jimai 44 and the strong gluten Jimai 229. Results(cid:0)Comparisons were also made with the winter wheat Jimai 22 as a control. It was found that there were 120 differentially expressed proteins between Jimai 44 and Jimai 22, while 173 between Jimai 229 and Jimai 22. Among these, 27 proteins were up-regulated and 23 down-regulated in jimai 44 and in jimai 229. Conclusions(cid:0)GO analysis and KEGG pathway analysis showed that the differentially expressed proteins were mainly enriched in cell functions relating to nutrient reservoir activity, metabolic processes, protein processing in endoplasmic reticulum, phenylpropane biosynthesis and nitrogen metabolism. Amongst the screened proteins, many differences were related to storage proteins. This study provides an important basis for further exploring the key factors of quality improvement and genetic variation of gluten protein expression.


Abstract
Background To determine the grain proteome of the strong gluten wheat, TMT technology was used to analyze proteomic differences between the winter wheat varieties Jimai 44 and the strong gluten Jimai 229.
Results Comparisons were also made with the winter wheat Jimai 22 as a control. It was found that there were 120 differentially expressed proteins between Jimai 44 and Jimai 22, while 173 between Jimai 229 and Jimai 22. Among these, 27 proteins were up-regulated and 23 down-regulated in jimai 44 and in jimai 229.
Conclusions GO analysis and KEGG pathway analysis showed that the differentially expressed proteins were mainly enriched in cell functions relating to nutrient reservoir activity, metabolic processes, protein processing in endoplasmic reticulum, phenylpropane biosynthesis and nitrogen metabolism. Amongst the screened proteins, many differences were related to storage proteins. This study provides an important basis for further exploring the key factors of quality improvement and genetic variation of gluten protein expression.

Background
Wheat is an important nutrient source for humans and about one third of the world's population use it as their main form of food intake. Therefore, understanding its protein content and quality is extremely important [1] .
Wheat quality is the key indicator and main purpose of wheat quality breeding [2] . Wheat quality mainly refers to the end-use quality of our [3][4][5] . The content of gluten protein affects the physical and chemical characteristics of wheat our and the quality of food processing to bake bread and steamed bread. Although the gene expression of gluten protein is mainly regulated at the transcriptional level, proteomic information plays an important role in studying the effect of different gluten protein types on dough characteristics and the accumulation level of different gluten proteins in wheat [6][7] .
Developing proteomic approaches provides the possibility to study the dynamic changes of gluten protein components and characteristics closely related to the processing performance of wheat our. Furthermore, it also provides a theoretical basis for improving the breeding processes and quality of wheat varieties.
Proteomics research can be used to separate gluten proteins by two-dimensional gel electrophoresis  or liquid chromatography for mass spectrometry analysis [8][9][10][11][12] . Dupont et al. [13] detected the expression of 5 variants of HMW-GS, 22 variants of LMW-GS, 23 variants of α -gliadin, 13 variants of γ -gliadin and 7 variants of ω -gliadin in our. Although Cho et al. [14] did not associate glutenin with its coding gene, 23 variants of α-gliadin, 11 variants of γ-gliadin and 5 variants of ω-gliadin were identi ed in Keumkang varieties in Korea. Dong [15] detected 11 variants of LMW-GS in grains of Xiaoyan 54, which supports the transcriptomic and proteomic data. Other studies found that environmental factors or tillage practices may cause a small part of gluten expression. Drought or salt stress tend to increase the accumulation of α -and ω -gliadin and HMW-GS, while the expression of α -gliadin seems to be more affected by heat and drought, indicating that this type of gliadin protein regulation is more sensitive to abiotic stress [16][17][18][19] .
To date, the genomic analysis of wheat gluten has been extensively studied. Identifying the proteome of wheat gluten will provide new directions for wheat quality-related functional genomics. This study intends to analyze the proteomics of the strong gluten wheat varieties Jimai 44 and Jimai 229 to further explore the key factors related to wheat quality improvement. By effectively utilizing the genetic variations of gluten protein structures and expressions in wheat grains, this data could provide important selection criteria for the cultivation and quality improvement of the strong gluten wheat varieties.

Results And Discussion
Identi cation and quanti cation of differentially expressed proteins in wheat grain The results of TMT proteomics analysis were obtained by matching with the wheat protein database. 6,627 proteins were identi ed in the sample grains of which 5,428 proteins were quanti able. Next, based on the protein abundance level and a difference of at least 1.5 folds, these proteins were screened and analyzed.  (Table 1). There were 7 kinds of differential proteins in Jimai 44 and Jimai 22. There were 35 differential proteins in metabolic process types, accounting for 31% of the total differential proteins. There were also many differential proteins in cell processes and single-cell biological process type, accounting for 21% and 16% of the total differential proteins respectively. Among them, metabolic processes (35) and cell processes (23) are the most important biological processes. Amongst the biological process categories of differentially expressed proteins in Jimai 229 and Jimai 22, the metabolic process (50) and cell process (41) were also the most important biological processes, and the number of differential proteins accounted for more than that of Jimai 44.The molecular function categories of the differential proteins of Jimai 44 and Jimai 22 mainly included catalytic activity (37), binding (25), nutrient storage activity (20), molecular function regulation (9), antioxidant activity (6), transport activity (2) and other activities of which the catalytic activity and binding occupy the two largest functional categories. There were mainly 5 types of molecular functional categories of the differentially expressed protein molecules of Jimai 229 and Jinmai 22, catalytic activity (60), binding (57), nutrient storage activity (24), molecular function regulation (14) and antioxidant activity (6), of which catalytic activity (60) and binding (57) are the two largest functional categories. There are no differential proteins related to transport activity in Jimai 229 and Jimai 22. In the cell composition category of Jimai 44 and Jimai 22, the differential proteins were primarily located in the intercellular region (11) and the cell membrane (8). In the cell composition category of Jimai 229 and Jimai 22, the differential proteins were mainly located in cells (20) and extracellular regions (12). According to the GO functional enrichment analysis, the differentially expressed proteins between the strong gluten wheat Jimai 44 and Jimai 229 were most abundant in nutrient reservoir activity when compared with the common wheat Jimai 22, which was related to quality formation and was also the main difference between the strong gluten wheat and the common wheat.    The results of hierarchical clustering analysis based on GO function are shown in Fig. 4 and Fig. 5. In the molecular function category, the up-regulated proteins in the differential proteins ( Fig. 4-A) of the strong gluten wheat Jimai 44 and Jimai 22 were mainly related to nutrient reservoir activity, 6-phosphate fructokinase activity and phosphofructose fructokinase activity, which were related to quality protein formation and energy metabolism. The down-regulated proteins were mainly related to nutrient reservoir activity. The differential proteins between the strong gluten wheat Jimai 229 and Jimai 22 were mainly related to nutrient reservoir activity (Fig. 5-A). The up-regulated proteins were mainly related to the nutrient reservoir activity, protein heterodimerization activity, protein dimer activity, structural molecular activity and other molecular functions, while the down-regulated proteins were mainly related to the nutrient reservoir activity. In the category of biological processes, the up-regulated differential proteins in Jimai 44 and Jimai 22 (Fig. 4-B) have a strong correlation with biological processes such as the negative regulation of protein metabolic processes, regulation of cellular metabolic processes, nucleoside phosphate metabolic processes and ribose phosphate metabolic processes. In comparison, most of the down-regulated differential proteins are related to biological processes such as cellular oxidant detoxi cation and cellular detoxi cation. Amongst the differentially expressed proteins of the strong gluten wheat Jimai 229 and Jimai 22 (Fig. 5-B), the upregulated differential proteins were related to the regulation of protein metabolic processes, proteolysis and the negative regulation of protein metabolic processes. while most of the down-regulated differential proteins were related to cell defense. The analysis of cell composition categories showed that the differential proteins of Jimai 44 were related to extracellular regions ( Fig. 4-C) and the differential proteins of Jimai 229 were mainly related to the chromosomes, nucleosome regions and cell regions (Fig. 5-C).
The results of layer clustering analysis based on KEGG are shown in Figure 6 which may be one of the internal factors in uencing the quality of Jimai 44. The low-molecular-weight glutenin subunit is the main component of glutenin and its content is higher than that of the high-molecularweight glutenin subunit. It mainly improves dough strength and ductility. We found that LMW-glutenin ( B2Y2Q1), controlled by GluB3-1, is up-regulated by 2.08 fold in Jimai 229 and down-regulated in Jimai 44.
Previous studies have shown that the b and d allelic variation of lu-b3 locus played a more important role in the expression of the doughs physical and chemical properties [22] . This subunit expression may be one of the reasons that the bread and noodle scores of Jimai 229 is better. This study found that Alpha-gliadin (A0A0E3Z7G8, A0A0K2QJX7, A0A0E3Z6M6, I0IT52, X2KVH9), controlled by Gli-2, was up-regulated by more than 4 folds in Jimai 44, and the expression level was higher than Jimai 229, which is consistent with the result of high gliadin content in Jimai 44 [20] .
The high-quality strong gluten wheat Jimai 229 and Jimai 44 contain high-quality subunits, and the differential protein expression related to gluten protein is higher than that of the common wheat Jimai 22, resulting in better quality characteristics of Jimai 229 and Jimai 44 compared to Jimai22. At the same time, Jimai 44 and Jimai 229 contain different expression levels of differential proteins, which contributes to some differences in the quality traits of Jimai 44 and Jimai 229. On the basis of these differential proteins, the quality-related traits can be veri ed by mining the regulated related genes, which provides more accurate theoretical data to support the development of wheat quality and the cultivation of high-quality varieties.

Protein Extraction
The sample was grinded by liquid nitrogen into cell powder and then transferred to a 5-mL centrifuge tube.
After that, four volumes of lysis buffer (8 M urea, 1% Triton-100, 10 mM dithiothreitol, and 1% Protease Inhibitor Cocktail) was added to the cell powder, followed by sonication three times on ice using a high intensity ultrasonic processor (Scientz). The remaining debris was removed by centrifugation at 20,000 g at 4°C for 10 min. Finally, the protein was precipitated with cold 20% TCA for 2 h at -20 °C. After centrifugation at 12,000 g 4 °C for 10 min, the supernatant was discarded. The remaining precipitate was washed with cold acetone for three times. The protein was redissolved in 8 M urea and the protein concentration was determined with BCA kit according to the manufacturer's instructions.

Trypsin Digestion
For digestion, the protein solution was reduced with 5 mM dithiothreitol for 30 min at 56 °C and alkylated with 11 mM iodoacetamide for 15 min at room temperature in darkness. The protein sample was then diluted by adding 100 mM TEAB to urea concentration less than 2M. Finally, trypsin was added at 1:50 trypsin-to-protein mass ratio for the rst digestion overnight and 1:100 trypsin-to-protein mass ratio for a second 4 h-digestion.

TMT Labeling
After trypsin digestion, peptide was desalted by Strata X C18 SPE column (Phenomenex) and vacuum-dried.
Peptide was reconstituted in 0.5 M TEAB and processed according to the manufacturer's protocol for TMT kit/iTRAQ kit. Brie y, one unit of TMT/iTRAQ reagent were thawed and reconstituted in acetonitrile. The peptide mixtures were then incubated for 2 h at room temperature and pooled, desalted and dried by vacuum centrifugation.

HPLC Fractionation
The tryptic peptides were fractionated into fractions by high pH reverse-phase HPLC using Agilent 300Extend C18 column (5 μm particles, 4.6 mm ID, 250 mm length). Brie y, peptides were rst separated with a gradient of 8% to 32% acetonitrile (pH 9.0) over 60 min into 60 fractions. Then, the peptides were combined into 18 fractions and dried by vacuum centrifuging.

LC-MS/MS Analysis
The tryptic peptides were dissolved in 0.1% formic acid (solvent A), directly loaded onto a home-made reversed-phase analytical column (15- We rst collated all the categories obtained after enrichment along with their P values, and then ltered for those categories which were at least enriched in one of the clusters with P value <0.05. This ltered P value matrix was transformed by the function x = −log10 (P value). Finally these x values were z-transformed for each functional category. These z scores were then clustered by one-way hierarchical clustering (Euclidean distance, average linkage clustering) in Genesis. Cluster membership were visualized by a heat map using the "heatmap.2" function from the "gplots" R-package.Only interactions between the proteins belonging to the searched data set were selected, thereby excluding external candidates. STRING de nes a metric called "con dence score" to de ne interaction con dence; we fetched all interactions that had a con dence score >0.7 (high con dence). Interaction network form STRING was visualized in R package "networkD3".

Declarations
Competing interests