The Level of Methionine Residues in Storage Proteins Is the Main Limiting Factor of Protein-Bound-Methionine Accumulation in Arabidopsis Seeds

Abstract

The low level of methionine, an essential sulfur-containing amino acid, limits the nutritional quality of seeds. Two main factors can control the level of protein-bound methionine: the level of free methionine that limits protein accumulation and the methionine residues inside the storage proteins. To reveal the main limiting factor, we generated transgenic Arabidopsis thaliana seed-specific plants expressing the methionine-rich sunflower seed storage (SSA) protein (A1/A2). The contents of protein-bound methionine in the water-soluble protein fraction that includes the SSA in A1/A2 were 5.3- and 10.5-fold, respectively, compared to control, an empty vector (EV). This suggests that free methionine can support this accumulation. To elucidate if the level of free methionine could be increased further in the protein-bound methionine, these lines were crossed with previously characterized plants having higher levels of free methionine in seeds (called SSE). The progenies of the crosses (A1S, A2S) exhibited the highest level of protein-bound methionine, but this level did not differ significantly from A2, suggesting that all the methionine residues of A2 were filled with methionine. It also suggests that the content of methionine residues in the storage proteins is the main limiting factor. The results also proposed that the storage proteins can change their content in response to high levels of free methionine or SSA. This was assumed since the water-soluble protein fraction was highest in A1S/A2S as well as in SSE compared to EV and A1/A2. By using these seeds, we also aimed at gaining more knowledge about the link between high free methionine and the levels of metabolites that usually accumulate during abiotic stresses. This putative connection was derived from a previous analysis of SSE. The results of metabolic profiling showed that the levels of 29 and 20 out of the 56 metabolites were significantly higher in SSE and A1, respectively, that had higher level of free methionine, compared A1S/A2S, which had lower free methionine levels. This suggests a strong link between high free methionine and the accumulation of stress-associated metabolites.

Keywords: 2S sunflower albumin; cystathionine-γ-synthase; metabolic profiling; metabolism; methionine; seed storage proteins.

Figure 1

Methionine metabolism in plants. Only some of the enzymes and metabolites are specified. Solid arrows represent one metabolic step while dashed arrows represent multiple metabolic steps. Abbreviations: AK, aspartate kinase; CGS, cystathionine γ-synthase; MGL, methionine γ-lase; SAM, S-adenosyl-methionine; S^-2, sulfide; OAS, O-acetylserine; SAT, serine acetyl transferase; OASTL, O-acetylserine thiol lyase.

Figure 2

The expression levels of SSA in transgenic seeds. (A) Immunoblot analysis of SSA in A1/A2 transgenic seeds expressing this gene. The upper panel represents the signal using antibodies against the SSA protein, while the bottom panel represents crude protein extracts counterstained with coomassie brilliant blue used for equal loading. The marker size is shown on the left. (B) qRT-PCR analysis of SSA transcript levels in lines expressing the SSA gene (A1, A2) and in the empty vector (EV) line used as a control. The results are normalized according to the expression of constitutive gene AtPP2A-A3. The data are presented as the mean ± SD of five individual plants per line. Statistically significant changes (Tukey-Kramer HSD test, P ≤ 0.05) between plants are identified by different letters.

Figure 3

Immunoblot analysis of SSA in transgenic seeds expressing SSA alone (A1, A2) and in seeds expressing SSA and AtD-CGS (A1S, A2S). For the analysis, water-soluble proteins were extracted from an equal amount of 20 mg seeds from each sample with the addition of an equal amount of extraction buffer. Two lines for each genotype were tested. Upper panels: immunoblot analysis using antibodies against the protein of SSA. Lower panel: coomassie brilliant blue staining used for equal loading. The marker size is shown on the left. The graph represents band intensity of the SSA normalized to a band from coomassie (marked by an arrow) as measured by ImageJ. The two gels were run separately.

Figure 4

Total water-soluble protein fractions analyzed by Bradford assay. Water-soluble proteins fractions in seeds expressing the SSA gene (A1, A2), in seeds expressing SSA and AtD-CGS (A1S, A2S), and in SSE and EV. The assay was done on extracts from 20 mg seeds/dry weight. The data are presented as the mean ± SD from four individual plants. Statistically significant changes (Tukey-Kramer HSD test, P ≤ 0.05) between plants are identified by different letters.

Figure 5

GC-MS analyses of free and protein-bound methionine and other amino acids. (A, B) Protein-bound levels of methionine and total amino acids in the transgenic dry seeds of A1S, A2S, A1, A2, SSE, and EV followed by protein hydrolysis; (C, D) Free levels of methionine and total amino acids in the same set of seeds. Data shown are means ± SD of five biological replicates. Statistically significant changes (Tukey-Kramer HSD test, P ≤0.05) between plants are identified by different letters.

Figure 6

Graphical representation of changes in metabolite profiles in the transgenic seeds. (A) Principal component analysis (PCA) according to their entire primary metabolome set of 56 metabolites measured by GC-MS analysis. The data points are displayed as projections onto the two primary axes (eigenvectors). Variances explained by the first two components (PC1 and PC2) appear in parentheses. (B) Schematic representation of these metabolites in a heat map. The intensity of blue or red represents the value of the coefficient, as indicated on the color scale. The data are represented as the average value of five biological replicates.

Figure 7

Seed weight and germination efficiency. (A) The seed weight of 100 seeds of the different transgenic lines of EV, SSE, A1, A2, A1S, and A2S. Data shown are means of four biological replicates. (B) The germination rate on the first and second days after the plates were placed for 2 days in a cold room and were then transferred to a culture room. The rate of germination was determined by observing the emergence of radicals. Data shown are means of five replicates, each composed of 30 seeds from each genotype. Statistically significant changes (Tukey-Kramer HSD test, P ≤0.05) between plants are identified by different letters.