Drought induced metabolic shifts and water loss mechanisms in canola: role of cysteine, phenylalanine and aspartic acid

Abstract

Drought conditions severely curtail the ability of plants to accumulate biomass due to the closure of stomata and the decrease of photosynthetic assimilation rate. Additionally, there is a shift in the plant's metabolic processes toward the production of metabolites that offer protection and aid in osmoadaptation, as opposed to those required for development and growth. To limit water loss via non-stomatal transpiration, plants adjust the load and composition of cuticle waxes, which act as an additional barrier. This study investigates the impact of soil water deficit on stomatal and epicuticular water losses, as well as metabolic adjustments in two canola (Brassica napus L.) cultivars-one drought-tolerant and the other drought-sensitive. Specifically, we examined the effect of a drought treatment, which involved reducing water holding capacity to 40%, on the levels of cysteine, sucrose, and abscisic acid (ABA) in the leaves of both cultivars. Next, we looked for potential differences in night, predawn, and early morning transpiration rates and the epicuticular wax load and composition in response to drought. A substantial rise in leaf cysteine was observed in both canola cultivars in response to drought, and a strong correlation was found between cysteine, ABA, and stomatal conductance, indicating that cysteine and sulfur may play a role in controlling stomatal movement during drought stress. Attributes related to CO₂ diffusion (stomatal and mesophyll conductance) and photosynthetic capacity were different between the two canola cultivars suggesting a better management of water relations under stress by the drought-tolerant cultivar. Epicuticular waxes were found to adjust in response to drought, acting as an additional barrier against water loss. Surprisingly, both canola cultivars responded similarly to the metabolites (cysteine, sucrose, and ABA) and epicuticular waxes, indicating that they were not reliable stress markers in our test setup. However, the higher level of phenylalanine in the drought-tolerant canola cultivar is suggestive that this amino acid is important for adaptation to drier climates. Furthermore, a multitrait genotype-ideotype distance index (MGIDI) revealed the likely role of aspartic acid in sustaining nitrogen and carbon for immediate photosynthetic resumption after drought episodes. In conclusion, leveraging amino acid knowledge in agriculture can enhance crop yield and bolster resistance to environmental challenges.

Keywords: ABA; Brassica napus; amino acids; aspartic acid; cysteine; drought; epicuticular waxes; phenylalanine.

Figure 1

Gravimetrically measured day and night water loss of drought-tolerant (DT) and drought-sensitive (DS) canola cultivars grown in pots. (A) The total amount of water lost per pot was measured every 2 h. The gray background represents nighttime (21:00 pm to 5:00 am) and the observations were recorded for two full nights and one full day. Data points represent mean ± SE (n = 5 plants). Statistical significance between DT and DS is indicated by asterisks (p < 0.01**). (B) Day and night average water loss per hour. The statistically significant differences between the cultivars during the day and night are labeled with different letters at p <0.05 (Tukey’s HSD).

Figure 2

Stomatal conductance (g _s) during the night, predawn, and early morning for the drought-tolerant (DT) and drought-sensitive (DS) canola cultivars under well-watered (A) and drought conditions (B). The statistically significant differences between the cultivars at different time points are labeled with different letters at p <0.05 (Tukey’s HSD).

Figure 3

Scatter plot showing linear association between cysteine and stomatal conductance (A), ABA and stomatal conductance (B), sucrose and net assimilation rate (C), and sucrose and dark respiration (D). Pearson correlation coefficient (R) and p-value for each linear relation are illustrated. The gray shading represents the 95% confidence interval around the line of the best fit.

Figure 4

SEM morphology of adaxial (A) and abaxial (B) leaf surfaces from well-watered and drought canola cultivars. Leaf surface wax elements were detected at ×5,000 magnification (scale bars = 2 µm). The CH₂/CH₃ ratio (C), stomatal pore opening represented as (l/b) ratio of pore length and width in adaxial (D) and abaxial (E) leaf surfaces for the drought-tolerant (DT) and drought-sensitive (DS) canola cultivars under well-watered and drought conditions. Data were analyzed using two-way ANOVA with post-hoc Tukey tests (letters indicate significant differences between groups at p < 0.05). Error bars depict standard deviation. Asterisks represent statistically significant differences between treatment and genotype (*p < 0.05, **p < 0.01, ***p < 0.001).

Figure 5

Leaf amino acid composition for the drought-tolerant (DT) and drought-sensitive (DS) canola cultivars under well-watered, drought, and water recovery conditions. Data were analyzed using two-way ANOVA with post-hoc Tukey tests (letters indicate significant differences between groups at p < 0.05). Error bars depict standard deviation. Amino acids are grouped based on different intermediates of the carbon metabolism pathway. Some intermediate components of the pathway are omitted for convenience.

Figure 6

Amino acid ranking in ascending order for a multitrait genotype-ideotype distance index (MGIDI) from well-watered to drought conditions (A) and from drought to recovery (B). The selected amino acids are shown in red circles.

Figure 7

Correlation matrix of the physiological traits, wax load, and metabolites for drought-tolerant (DT) and drought-sensitive (DS) canola cultivars under well-watered and drought conditions. The increasing intensity of color and size of the bubble indicates positive (purple) and negative (brown) Pearson’s correlation.

Figure 8

Pathway model hypothesized from agronomic, physiological, and metabolite traits for drought-tolerant (DT) and drought-sensitive (DS) canola cultivars under well-watered and drought conditions. Values on the arrow are path coefficients. Blue arrows indicate positive effects and red arrows indicate negative effects.