Metabolic Signatures in Response to Abscisic Acid (ABA) Treatment in Brassica napus Guard Cells Revealed by Metabolomics

Abstract

Drought can severely damage crops, resulting in major yield losses. During drought, vascular land plants conserve water via stomatal closure. Each stomate is bordered by a pair of guard cells that shrink in response to drought and the associated hormone abscisic acid (ABA). The activation of complex intracellular signaling networks underlies these responses. Therefore, analysis of guard cell metabolites is fundamental for elucidation of guard cell signaling pathways. Brassica napus is an important oilseed crop for human consumption and biodiesel production. Here, non-targeted metabolomics utilizing gas chromatography mass spectrometry (GC-MS/MS) and liquid chromatography mass spectrometry (LC-MS/MS) were employed for the first time to identify metabolic signatures in response to ABA in B. napus guard cell protoplasts. Metabolome profiling identified 390 distinct metabolites in B. napus guard cells, falling into diverse classes. Of these, 77 metabolites, comprising both primary and secondary metabolites were found to be significantly ABA responsive, including carbohydrates, fatty acids, glucosinolates, and flavonoids. Selected secondary metabolites, sinigrin, quercetin, campesterol, and sitosterol, were confirmed to regulate stomatal closure in Arabidopsis thaliana, B. napus or both species. Information derived from metabolite datasets can provide a blueprint for improvement of water use efficiency and drought tolerance in crops.

Figure 1

Responses to ABA in B. napus leaves, epidermal peels, and guard cell protoplasts. (A) ABA (10 µM) induces stomatal closure in both leaf pieces (left panel) and epidermal peels (right panel) of B. napus line DH12075. Data are means ± standard errors of 3 independent replicates with 100 ± 5 stomata measured for each sample. (B) ABA-induced shrinkage of B. napus GCPs. Representative image (left); scale bar indicates 25 µm. Data (right) are means ± standard errors of 4 independent replicates with 100 ± 5 GCPs measured for each sample. (C) B. napus GCPs are viable following ABA or ethanol (solvent control) treatment. Samples before treatment (0 min) and after treatment (ethanol (EtOH) 60 min and ABA 60 min) were FDA stained to assess cell viability. Scale bars indicate 10 µm. Asterisks in A and B indicate that ABA treatment differed significantly from the EtOH solvent control (Student’s t test; p < 0.05).

Figure 2

Metabolomic profiling using complementary platforms resulted in identification of 390 non-redundant metabolites in B. napus GCPs. (A) Classification of metabolites identified from each platform, mainly based on structural characteristics. The metabolite categories are listed in the figure caption in the clock-wise order in which they appear in the figure, starting with “Proteinogenic amino acids” at the 12 o’clock position (arrow) in all four pie charts. No metabolites in the category of “Amines and polyamines” were identified by the LC-MS (−) platform. No metabolites were identified by GC-MS in the categories of “Cofactors”, “Non-proteinogenic amino acids”, “Alkaloids”, or “Sulfur-containing”. (B) Venn diagram showing the number of metabolites identified from each platform.

Figure 3

Primary (A) and secondary (B) metabolites responsive to ABA at different time points in B. napus GCPs. At 2, 15, and 60 min heat maps represent log2 of fold change, i.e., the log2-transformed metabolite abundance (peak area) at each time point divided by the level at 0 min; a 0 min column is also provided for comparison. All metabolites depicted were significantly changed at one or more time points (2 min, 15 min, and 60 min) of ABA treatment. Abbreviations: UDP: uridine diphosphate; AICAR: 5-aminoimidazole-4-carboxamide-1-ribofuranosyl.

Figure 4

Metabolic pathways affected by ABA treatment in guard cells revealed by pathway analysis. x axis represents the impact of the identified metabolites on the indicated pathway. y axis indicates the extent to which the designated pathway is enriched in the identified metabolites. Values were ascertained from MetaboAnalyst. Circle colors (see color scale for reference) indicate pathway enrichment significance. Circle size indicates pathway impact.

Figure 5

Abundance changes along the time course of ABA treatment for quercetin and quercetin derivatives (A), non-quercetin flavonoids (B), and glucosinolates (C). Metabolites 1–22 are: 1: quercetin-3-(6″-malonyl)-glucoside; 2: quercetin; 3: quercetin-3-arabinoside; 4: quercetin-3,4′-O-di-beta-glucopyranoside; 5: quercetin-4′-glucoside; 6: myricetin-3-galactoside; 7: kaempferol; 8: kaempferol-3-O-glucoside; 9: cyanidin-3,5-di-O-glucoside; 10: hesperetin; 11: isosakuranetin-7-O-neohesperidoside; 12: cyanidin-3-sophoroside; 13: hesperidin; 14: naringin; 15: cyanidin-3-O-galactoside; 16: 3-hydroxy-3′,4′,5′-trimethoxyflavone; 17: myricetin; 18: 7-methylthioheptyl glucosinolate; 19: 8-methylthiooctyl glucosinolate; 20: (2 R)−2-hydroxy-2-phenethylglucosinolate; 21: 4-methylsufinyl-3-butenyl glucosinolate. Solid data points indicate statistically significant changes upon ABA treatment (Student’s t test; p value < 0.05) compared to 0 min data.

Figure 6

Effects of quercetin, sinigrin, campesterol, and β-sitosterol on stomatal apertures in A. thaliana (A,C,E and G) and B. napus (B,D,F and H) leaves. Data are means ± standard errors of at least 4 independent replicates with 100 ± 5 stomata measured for each sample. Asterisks indicate a significant effect of addition of the secondary metabolite (Student’s t test; p < 0.05).