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. 2021 Mar 24;22(7):3294.
doi: 10.3390/ijms22073294.

Metabolite Analysis of Jerusalem Artichoke (Helianthus tuberosus L.) Seedlings in Response to Polyethylene Glycol-Simulated Drought Stress

Mengliang Zhao  1   2   3 , Yanjing Ren  2   3 , Wei Wei  1 , Jiaming Yang  1 , Qiwen Zhong  2   3 , Zheng Li  1
Affiliations

Metabolite Analysis of Jerusalem Artichoke (Helianthus tuberosus L.) Seedlings in Response to Polyethylene Glycol-Simulated Drought Stress

Mengliang Zhao et al. Int J Mol Sci. .

Abstract

Jerusalem artichokes are a perennial crop with high drought tolerance and high value as a raw material to produce biofuels, functional feed, and food. However, there are few comprehensive metabolomic studies on Jerusalem artichokes under drought conditions.

Methods: Ultra-performance liquid chromatography and tandem mass spectrometry were used to identify differential metabolites in Jerusalem artichoke seedling leaves under polyethylene glycol (PEG) 6000-simulated drought stress at 0, 18, 24, and 36 h.

Results: A total of 661 metabolites and 236 differential metabolites were identified at 0 vs. 18, 18 vs. 24, and 24 vs. 36 h. 146 differential metabolites and 56 common were identified and at 0 vs. 18, 24, and 36 h. Kyoto Encyclopedia of Genes and Genomes enrichment identified 236 differential metabolites involved in the biosynthesis of secondary metabolites and amino acids. Metabolites involved in glycolysis, phenolic metabolism, tricarboxylic cycle, glutamate-mediated proline biosynthesis, urea cycle, amino acid metabolism, unsaturated fatty acid biosynthesis, and the met salvage pathway responded to drought stress.

Conclusion: A metabolic network in the leaves of Jerusalem artichokes under drought stress is proposed. These results will improve understanding of the metabolite response to drought stress in Jerusalem artichokes and develop a foundation for breeding drought-resistant varieties.

Keywords: Jerusalem artichoke; drought stress; metabolic network; metabolism.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Physiological changes of Jerusalem artichoke in response to drought stress. (a) Changes of the stem and leaves dry weight/fresh weight from 0 to 48 h; (b) changes of the stem and leaves fresh weight/root fresh weight from 0 to 48 h; (c) changes of the water content of stems and leaves from 0 to 48 h; (d) changes of the survival rate in the Jerusalem artichoke from 0 to 48 h.
Figure 2
Figure 2
The morphology of Jerusalem Artichokes treated with drought stress, and the heatmap of correlation and principal component analysis (PCA) between different drought treatment and quality control (QC) samples. (a) 0 h; (b) 18 h; (c) 24 h; (d) 36 h; (e) Principal component analysis. (f) Heatmap of correlation.
Figure 3
Figure 3
Orthogonal partial least squares-discriminant analysis (OPLS–DA) scores. Scores of the OPLS–DA model with (a) 0 vs. 18 h, (b) 18 vs. 24 h, (c) 24 vs. 36 h. OPLS–DA S-plot model with (d) 0 vs. 18 h, (e) 18 vs. 24 h, (f) 24 vs. 36 h. R2 Y scores and Q2 values represent the interpretation rate of the model to the Y matrix and the prediction ability of the model, respectively. When Q2 > 0.5, the model can be considered an effective model, and Q2 > 0.9 is an excellent model.
Figure 4
Figure 4
Differential metabolite analysis of Jerusalem artichokes under drought conditions. Volcano maps of differential metabolites in different pairwise comparisons: (a) 0 vs. 18 h; (b) 18 vs. 24 h; (c) 24 vs. 36 h; (d) 0 vs. 24 h; (e) 0 vs. 36 h; (f) 18 vs. 36 h. (g) Venn diagram of differential metabolites in a multiple pairwise comparison of 0 vs. 18 h, 18 vs. 24 h, and 24 vs. 36 h. (h) Venn diagram of differential metabolites in multiple pairwise comparisons of 0 vs. 18 h, 0 vs. 24 h and 0 vs. 36 h. (i) Trend analysis of differential metabolites during the treatment of drought stress from 0 to 36 h.
Figure 5
Figure 5
Analysis of metabolic networks in the leaves of Jerusalem artichoke under drought stress. Proposed metabolic pathways were based on the literature and web-based database of metabolic pathways. The metabolites written in gray were not detected in this study. The differential metabolite changes were represented by the log2 ratio. Blue represents a decrease in content and red represents an increase in content. * indicates a significant difference.
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