Identification of differentially abundant proteins (DAPs)
Comparative proteomic analysis was used to identify the changes of protein profiles in contrasting barley genotypes differing in water stress response and remobilization characteristics. Our results showed 309 increased proteins in abundance (Fig. 4a) and 450 decreased proteins in abundance (Fig. 4c) at 21 DAA. 11 stress-responsive shared proteins were found in Morocco and PBYT17 barley genotypes. Seven proteins were shared in both Morocco and Yousef genotypes (Fig. 4a). At 21 DAA, 8 and 16 stress-responsive shared proteins were observed between the Yousef and PBYT17, and between the Morocco genotypes, respectively. Only one common protein was identified among the three genotypes (Fig. 4c). At 21 DAA, PBYT17 and Morocco genotypes showed the highest number of uniquely differentially abundant proteins whereas the tolerant genotype displayed the lowest number. Then, functional classification was performed to explore the molecular function of significantly changed proteins in response to water stress in each single genotype. The major functional categories of increased protein in abundance in Yousef genotype was as follows: 14.2% in carbohydrate metabolism, 5% in lipid metabolism, 8% in amino acid metabolism, 14.2% in both water stress and protein metabolism (synthesis and degradation), 5% each in transport, miscellaneous functions, RNA, and DNA, and 2% in each of secondary metabolism and cell wall degradation. In contrast, in Morocco genotype, increased proteins in abundance were more implicated in carbohydrate metabolism (6%), protein metabolism (40.46%), amino acid metabolism (8%), redox (4%), misc (8%), signalling (10%), nucleotide metabolism (4%), RNA (6%), and DNA (6%). In the PBYT17 genotype, the largest portion of the increased proteins in abundance was involved in carbohydrate metabolism (7.5%), mitochondrial electron transport/ATP synthesis (28%), lipid metabolism (2.8%), amino acid metabolism (3.7%), hormone metabolism (4.7%), stress (4.7%), mitochondrial 2-oxoglutarate/malate carrier protein (misc) (8.4%), proteins metabolism (synthesis and degradation) (25%), and cell (5.6%) (Fig. 4b). The majority of proteins that decreased in abundance in Yousef genotype 21 DAA was included 7.5% in carbohydrate metabolism, 6.7% in lipid metabolism, 8.4% in amino acid metabolism, 3.3% in each of RNA and DNA metabolism, 20.3% in protein metabolism (synthesis and degradation), 5% in the cell wall (degradation and modification), signalling and cell, and 15.2% in misc. Whereas, in Morocco genotype, the decreased proteins in abundance were functionally categorised in carbohydrate metabolism (18.7%), protein metabolism (18%), lipid metabolism (3.7%), amino acid metabolism (2.2%), secondary metabolism (4.5%), redox (3%), stress (7.5%), signalling (2.2%), RNA (10.5%) and cell (3.7%), and misc (7.5%). In the PBYT17 genotype, the largest portion of the decreased protein in abundance was functionally distributed in carbohydrate metabolism (9.7%), cell wall (3.2%), lipid metabolism (3.2%), amino acid metabolism (4.3%), secondary metabolism (2%), stress (3.2%), redox (6.5%), nucleotide metabolism (2.1%), DNA (1%), RNA (1%), protein metabolism (synthesis and degradation) (10.8%), signalling (5.4%), and misc (4.3%) (Fig. 4d). At 28 DAA, 420 increased proteins and 401 decreased proteins were observed in response to stress (Fig. 5a,c) of which 21 shared proteins were identified between Yousef and Morocco and and between the Morocco and PBYT17 genotypes (Fig. 5a). Only two shared common proteins were identified in three genotypes (Fig. 5c). Furthermore, 19 and 12 shared proteins were identified between Yousef and PBYT17, and between Yousef and Morocco genotypes, respectively (Fig. 5c). The highest number of DAPs also were observed in PBYT17 and Morocco genotypes. Yousef showed the lowest number DAPs.
The statistical results of increased (a) and decreased (c) in abundance at 21 days after anthesis (DAA) in Yousef, Morocco, and PBYT17 genotypes represented in an upset plot. The upset analysis revealed the proteins identified by each sample preparation method, as well as the unique proteins exclusively identified by a single method. Furthermore, an upset chart was utilized to illustrate the overlap in proteins identified by each protein processing method. The matrix rows are linked to show where the sets intersect while the vertical bar chart displays the quantity of proteins shared by those sets. The top bar chart reveals the counts of proteins identified across various methods, and the solid dots below the chart indicate which methods belong to each set. A pie chart was used to display the functional annotation of the increased (b) and decreased (d) proteins in abundance in Yousef, Morocco, and PBYT17 genotypes at 21 DAA.
The statistical results of increased (a) and decreased (c) in abundance at 28 days after anthesis (DAA) in Yousef, Morocco, and PBYT17 genotypes represented in an upset plot. The upset analysis revealed the proteins identified by each sample preparation method, as well as the unique proteins exclusively identified by a single method. Furthermore, an upset chart was utilized to illustrate the overlap in proteins identified by each protein processing method. The matrix rows are linked to show where the sets intersect while the vertical bar chart displays the quantity of proteins shared by those sets. The top bar chart reveals the counts of proteins identified across various methods, and the solid dots below the chart indicate which methods belong to each set. A pie chart was used to display the functional annotation of the increased (b) and decreased (d) proteins in abundance in Yousef, Morocco, and PBYT17 at 28 DAA.
The functional distribution of increased proteins in abundance in the Yousef genotype 28 DAA was as follows: carbohydrate metabolism (6.8%), protein metabolism (20.45%), signalling (6.8%), cell wall (9%), lipid metabolism (13.6), amino acid metabolism (6.8%), misc (6.8%), and RNA (6.8%). While in the Morocco genotype, increased proteins in abundance were more implicated in carbohydrate metabolism (8.89%), protein metabolism (24.47%), amino acid metabolism (4.49%), secondary metabolism (5.6%), and RNA (6.67%). In the PBYT17 genotype, increased proteins in abundance were categorized in carbohydrate metabolism (14.5%), lipid metabolism (6.45%), stress (4.82%), protein metabolism (11.2%), amino acid metabolism (8.06%), and RNA (6.45%) (Fig. 5b). The majority of proteins that decreased in abundance 28 DAA in Yousef genotype were involved in carbohydrate metabolism (18.27%), stress (4.3%), misc (7.5%), RNA (8.6%), protein metabolism (30.1%), signalling (3.2%), and lipid metabolism (3.2). However, in Morocco genotype, decreases protein in abundance were implicated in carbohydrate metabolism (5.8%), lipid metabolism (5.8%), stress (8.8%), misc (14.7%), protein metabolism (20.5%), nucleotide metabolism (5.8%), and RNA (8.8%). In contrast, in the PBYT17 genotype, decreased in abundance were categorized in carbohydrate metabolism (8.13%), lipid metabolism (4.06%), stress (4.8%), redox (3.25%), misc (6.5%), protein metabolism (20.3%), transport (5.6%), signalling (6.5%), and RNA (10.5%) (Fig. 5d).
Multivariate data analysis of DAPs
As a supervised method, partial least square discrimination analysis (PLS-DA) was used to predict or discriminate DAPs that are potentially useful biomarkers in helping sample classification 21 and 28 DAA under water stress using the variable of importance in prediction (VIP) score value (Figs. 6 and 7). We observe 335, 457, and 466 proteins with VIP greater than 1 in Yousef, PBYT-17, and Morocco, at 21 DAA respectively (Fig. 6a, c, e). These proteins were more functionally implicated in protein metabolism and carbohydrate metabolism. Interestingly 26,69%, 36.30%, and 37.04% of DAPs were amongst of the proteins with VIP > 1 at 21 DAA. However, at 28 DAA, 389, 442, and 348 protein with VIP greater than 1 were found in Yousef, PBYT-17, and Morocco, respectively (Fig. 7a, c, e). The functional groups analysis indicated these proteins were more classified in protein metabolism carbohydrate metabolism, and particularly RNA metabolism. The results showed 32.99%, 37.48%, and 29.51% of DAPs were amongst of proteins with VIP > 1 at 28 DAA.
Proteomics data from barley were subjected to multivariate statistical analysis. The results include score plots for Partial Least Square Discriminant Analysis (PLS-DA). The red group represents control samples that were control for 21 days, and the green group represents water-stressed samples (b, d, f). The MetaboAnalyst web-based platform was used to determine the Variable Importance in Projections (VIP) scores for the top proteins in the study (a, c, e), indicating their significance. The coloured boxes represent the abundance of these proteins in each group, with red and blue denoting an increase and a decrease, respectively.
Proteomics data from barley were subjected to multivariate statistical analysis. The results include score plots for Partial Least Square Discriminant Analysis (PLS-DA). The red group represents control samples that were control for 28 days, and the green group represents water-stressed samples (b, d, f). The MetaboAnalyst web-based platform was used to determine the Variable Importance in Projections (VIP) scores for the top proteins in the study (a, c, e), indicating their significance. The coloured boxes represent the abundance of these proteins in each group, with red and blue denoting an increase and a decrease, respectively.
Correlations between proteins and remobilization efficiency traits
To determine the protein expression patterns changes at 21 and 28 DAA in control and water stress conditions, we performed correlation analysis between the abundance of proteins and level of the remobilization efficiency of penultimate internodes. At 21 DAA, our findings revealed a total of 90 positive correlations alongside 115 negative correlations under control conditions, emphasizing the complex interplay between protein expression and remobilization efficiency (Fig. 8a and Supplementary data S1). The shift observed under water stress, with positive correlations increasing to 98 and negative correlations decreasing to 110, underscores the adaptability of protein expression in response to water availability (Fig. 8b and Supplementary data S1). Of the strongest correlated proteins with remobilization efficiency under water stress included trehalose-6 phosphate synthase (F4ZC54) (T6P), galactokinase (A0A8I6XZ94), two sucrose synthase (P31922 and F2DRP6), Inositol-1-monophosphatase (A0A8I6XZT0), xylose isomerase (Q40082), and aldose 1 epimerase family protein (F2D0E0) starch synthase (A0A8I6WY77), ribokinase (F2CTM5), and glucan synthase-like 3 (C6GFB3) involved in carbohydrate metabolism, two proteins involved in fermentation pathway such as alcohol dehydrogenase (ADH1) (Q94L27) and ADH4 (C5NM76) and 6-phosphogluconate dehydrogenase decarboxylating protein (A0A8I6Z047) involved in OPPP. Notably, the levels of galactokinase, sucrose synthase, inositol-1-monophosphatase and 6-phosphogluconate dehydrogenase decarboxylating protein decreased in abundance in the susceptible genotype but were unchanged in the tolerant one at 21 DAA under water stress (Fig. 8b). However, two ADH decreased in abundance in the susceptible genotype but increased in the tolerant one at 21 DAA under water stress (Fig. 8b).
A model for the roles of proteins and metabolites associated to remobilization efficiency. Circles with Red, blue, and white colours show increased, decreased, and maintained metabolites, respectively. Red, blue, and white squares indicate positive, negative, and non-significant correlations, respectively. (a) Proteins and metabolites associated with remobilization efficiency under control condition at 21 DAA. (b) Proteins and metabolites associated with remobilization efficiency under water stress condition at 21 DAA. (c) Proteins and metabolites associated with remobilization efficiency under control condition at 28 DAA. (d) Proteins and metabolites associated with remobilization efficiency under water stress condition at 28 DAA. Y: Yousef; M: Morocco; P: PBYT17. PFP: pyrophosphate-fructose-6-P phosphotransferase; PFK: Phosphofructokinase; IMP: Inositol-1-monophosphatase; SPS: sucrose phosphate synthase; OPPP: oxidative pentose phosphate pathway; PPP: Pentose phosphate pathway; Fru: Fructose; Glu: Glucose; G6p: Glucose 6-phosphate; Fru-6p: Fructose 6-phosphate; UDP-Glu: UDP-Glucose; Glu-1p: Glucose 1-phosphate; TPS: Trehalose 6 phosphate synthase; Susy: Sucrose synthase; PEPC: Phosphoenolpyruvate carboxylase; PK: Pyruvate kinase; PGK: Phosphoglycerate kinase; GAPDH: Glyceraldehyde 3-phosphate dehydrogenase; Fru2,6BisPase: Fructose-2,6-bisphosphatase; ADH: Alcohol dehydrogenase; SS: Starch synthase; IP3: Inositol; XYlA: Xylose imsomerase; XKS1: Xylose kinase; Xu5P: Xylulose-5-phosphate; Rpi: Ribose 5 phosphate isomerase; Ru-5P: Ribose 5 phosphate; 6PGD: 6-phosphogluconate dehydrogenase; 6PGDH: 6-phosphogluconate; PGLS: 6 phosphogluconolactonase; 6PG: 6-phosphoglucone; G6PD: Glucose 6 phosphate dehydrogenase; SUCLG: Succinyl CoA ligase; SDH: Succinate dehydrogenase; MDH: Malate dehyrogenas; IDH: Isocitrate dehydrogenase; FH: Fumarase; ACO: Aconitase; PYR: Pyruvate; GAP: Glyceraldehyde-3-phosphate; 1,3BPG: 1,3 bisphosphateglycerate; 3PGA: 3 phosphoeglycerate; 2PGA: 2 phosphoeglycerate; PGM: Phosphoglycerate mutase; ENV: Enolase; PDH: Pyruvate dehydrogenase; ACLY: ATP citrate synthase; GALK1: Galactokinase; GAL: Galactose; GlcA: Glucuronic acid; OG: Oxoglutarate; CA: Cis-AconitateCwInv: Cell wall invertase; CyInv: Cytoplasmic invertase; AG: Alpha glucosidase; FBA: Fructose-1,6-bisphosphate aldolase; Y21DN: Yousef under control condition at 21 days after anthesis; Y21DN: Yousef under water stress condition at 21 days after anthesis; P21DN: PBYT17 under control condition at 21 days after anthesis; P21DS: PBYT17 under water stress condition at 21 days after anthesis; M21DN: Morocco under control condition at 21 days after anthesis; M2DS: Morocco under stress condition at 21 days after anthesis; Y21DN: Yousef under control condition at 28 days after anthesis; Y21DN: Yousef under water stress condition at 28 days after anthesis; P28DN: PBYT17 under control condition at 28 days after anthesis; P28DS: PBYT17 under water stress condition at 28 days after anthesis; M28DN: Morocco under control condition at 28 days after anthesis; M28DS: Morocco under water stress condition at 21 days after anthesis.
As we progressed to the 28 DAA time point, the results under control conditions were even more pronounced, with 121 positive and only 40 negative correlations identified (Fig. 8c and Supplementary data S1). Conversely, under water-stressed conditions at 28 DAA, we observed a shift with 96 positive and 99 negative correlations (Fig. 8d and Supplementary data S1). Such findings underscore the necessity of further investigations into the molecular mechanisms governing these traits, as they hold significant implications for improving crop resilience and yield under adverse environmental conditions. A correlated proteins with remobilization efficiency under water stress at 28 DAA included Thioredoxin like 5 (F2DTW0), Ferredoxin thioredoxin reductase catalytic chain (A0A8I6Y6J4), Rubredoxin (F2CR00), Peroxidase superfamily protein LENGTH 329 (A0A8I6YEN8), Glutathione peroxidase (Q9SME6), Superoxide dismutase copper chaperone putative (F2E710), and NADH cytochrome b5 reductase (F2E5P4) involved in redox-related proteins, five ribosomal proteins including 30S ribosomal protein S19 (A0A191TDL1), 30S ribosomal protein S19(A0A8I6WXR4), 60S ribosomal protein L14 putative (A0A218LNP1), Ribosomal protein L19 (F2D9X8), 60S ribosomal protein L32 (S4Z0T4), 60S ribosomal protein L32 (A0A8F4MA56), and 60S ribosomal protein L7a (F2DE13), two proteins involved in RNA regulation in Remorin (A0A8I6YBT6) and the GAGA binding transcriptional activator (F2E947). Notably, NADH cytochrome b5 reductase levels significantly increased in the Yousef genotype at 28 DAA but remained unchanged in Morocco and PBYT17 genotypes, suggesting better ROS elimination and cellular homeostasis in the Yousef genotype (Supplementary data S1). Five ribosomal proteins increased in expression at 28 DAA in Morocco under water stress. While ribosomal proteins remained unchanged at 28 DAA in Yousef genotype under water stress condition (Fig. 8b,d). In the Morocco genotype, the abundance of ribosomal proteins, protein biosynthesis is an energy consuming process since all proteins need energy in the form of sugars during their biosynthesis. Furthermore, Remorin unchanged in the Yousef genotype but decreases in the Morocco genotype at 28 DAA under water stress.
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- Source: https://www.nature.com/articles/s41598-024-79598-3