Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (7): 1333-1343.doi: 10.3864/j.issn.0578-1752.2025.07.007

• PLANT PROTECTION • Previous Articles     Next Articles

Widespread Non-Targeted Metabolomics Reveals Metabolites of Chloroplasts in Wheat Responses to Stripe Rust

WU Yu(), QU XiangRu, YANG Dan, WU Qin, CHEN GuoYue, JIANG QianTao, WEI YuMing(), XU Qiang()   

  1. Triticeae Research Institute/State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130
  • Received:2024-12-06 Accepted:2025-02-22 Online:2025-04-08 Published:2025-04-08
  • Contact: WEI YuMing, XU Qiang

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Abstract:

【Objective】The purpose of this study is to elucidate the changes of chloroplast metabolites during wheat resistance to stripe rust, and to clarify the role of key chloroplast metabolites in wheat resistance to stripe rust.【Method】Wheat cultivar Suwon11 was used as the experimental material, wheat chloroplasts were extracted 48 and 72 h after spraying sterile water (control group) and Puccinia striiformis f. sp. tritici CYR23 (experimental group), and a widespread non-targeted metabolomics analysis was conducted via ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). Principal component analysis (PCA) and correlation analysis were employed to visualize inter-group and intra-group differences and associations. VIP (variable importance in projection) values derived from the orthogonal partial least squares discriminant analysis (OPLS-DA) model were employed to identify differential chloroplast metabolites between the disease-resistant and control groups. Pathway enrichment analysis was performed on differential chloroplast metabolites using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to investigate key metabolic pathways in chloroplasts during the resistance response of wheat to stripe rust.【Result】The metabolomics data exhibited high quality, with good intra-group reproducibility and significant inter-groups variability validated by multivariate statistical analyses. A total of 1 496 metabolites were detected, which predominantly included lipids and lipid-like molecules, organic acids and their derivatives, organic heterocyclic compounds, phenylpropanoids and polyphenolic compounds, organic oxides, and benzoid compounds. The metabolite species were generally similar between different groups, but significant variations in metabolite levels were observed at different infection time points. At 48 hours post infection (hpi) compared to the control group (CK), 121 differential metabolites were identified, including 21 up-regulated and 100 down-regulated metabolites; At 72 hpi compared to the CK, 58 differential metabolites were detected, with 35 up-regulated and 23 down-regulated; Between 72 and 48 hpi groups, 53 differential metabolites were observed, of which 33 were up-regulated and 20 were down-regulated. Four differential metabolites were shared between the 48 hpi vs CK and 72 hpi vs CK comparisons. KEGG analysis revealed that the differential metabolites were enriched in multiple metabolic pathways. Among them, the expression of linoleic acid which in the linoleic acid metabolic pathway exhibited 2.75- and 2.93- fold increases after inoculation 48 and 72 h, respectively, indicating that linoleic acid was continuously synthesized induced by P. striiformis f. sp. tritici.【Conclusion】The chloroplast metabolites associated with wheat resistance to stripe rust primarily include lipids, fatty acids and their derivatives, organic acids and their derivatives, organic oxides, as well as phenylpropanoids and polyphenolic compounds. Notably, linoleic acid may play a crucial role in the sustained resistance of wheat against stripe rust.

Key words: wheat (Triticum aestivum), stripe rust, Puccinia striiformis f. sp. tritici, chloroplast, metabonomics, linoleic acid

Fig. 1

The phenotype of wheat leaf infected by Pst CYR23 (A), correlation analysis (B) and principal component analysis (C)"

Fig. 2

OPLS-DA score plot among the three samples The horizontal coordinate T score [1] represents the predicted score value which represent the difference of metabolites within the group. The ordinate Othogonal T score [1] indicates the orthogonal component score, the percentage represents how well the metabolite interprets the all data"

Table 1

Classes and numbers of metabolites in wheat chloroplast"

种类Class 数量Number 占比Proportion (%)
脂质和类脂分子Lipids and lipid_like molecules 480 32.09
有机酸及其衍生物Organic acids and derivatives 277 18.52
其他Undefined 213 14.24
有机杂环化合物Organoheterocyclic compounds 127 8.49
有机氧化物Organic oxides 111 7.42
苯丙类和聚酮类物质Phenylpropanoids and polyketides 105 7.02
苯类化合物Benzoid compounds 101 6.75
有机氮氧化物Organic nitrogen oxides 31 2.07
核苷、核苷酸及类似物Nucleosides, nucleotides, and analogues 22 1.47
核苷酸类似物Nucleotides analogues 14 0.94
生物碱及其衍生物Alkaloids and derivatives 8 0.53
木脂素、新木脂素及相关化合物Lignans, neolignans and related compounds 3 0.20
有机硫化合物Organosulfur compounds 3 0.20
有机金属化合物Organometallic compounds 1 0.07

Fig. 3

Volcano plot of differential metabolites Blue dots indicate down-regulated metabolites, while red dots represent up-regulated metabolites, and black dots mean no difference"

Fig. 4

Venn diagram of differential metabolites The sum of the numbers in the circle indicates the number of differential metabolites shared between groups; numbers where different colors intersect indicate the number of common differential metabolites between the comparison groups"

Fig. 5

KEGG metabolic pathway enrichment bubble plot A: 48 hpi; B: 72 hpi"

Fig. 6

Fold changes of differential metabolites A: 48 hpi; B: 72 hpi"

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