Integrating Targeted Metabolomics and Targeted Proteomics to Study the Responses of Wheat Plants to Engineered Nanomaterials

Abstract

This manuscript presents a multiomics investigation into the metabolic and proteomic responses of wheat to molybdenum (Mo)- and copper (Cu)-based engineered nanomaterials (ENMs) exposure via root and leaf application methods. Wheat plants underwent a four-week growth period with a 16 h photoperiod (light intensity set at 150 μmol·m^-2·s^-1), at 22 °C and 60% humidity. Six distinct treatments were applied, including control conditions alongside exposure to Mo- and Cu-based ENMs through both root and leaf routes. The exposure dosage amounted to 6.25 mg of the respective element per plant. An additional treatment with a lower dose (0.6 mg Mo/plant) of Mo ENM exclusively through the root system was introduced upon the detection of phytotoxicity. Utilizing LC-MS/MS analysis, 82 metabolites across various classes and 24 proteins were assessed in different plant tissues (roots, stems, leaves) under diverse treatments. The investigation identified 58 responsive metabolites and 19 responsive proteins for Cu treatments, 71 responsive metabolites, and 24 responsive proteins for Mo treatments, mostly through leaf exposure for Cu and root exposure for Mo. Distinct tissue-specific preferences for metabolite accumulation were revealed, highlighting the prevalence of organic acids and fatty acids in stem or root tissues, while sugars and amino acids were abundant in leaves, mirroring their roles in energy storage and photosynthesis. Joint-pathway analysis was conducted and unveiled 23 perturbed pathways across treatments. Among these, Mo exposure via roots impacted all identified pathways, whereas exposure via leaf affected 15 pathways, underscoring the reliance on exposure route of metabolic and proteomic responses. The coordinated response observed in protein and metabolite concentrations, particularly in amino acids, highlighted a dynamic and interconnected proteomic-to-metabolic-to-proteomic relationship. Furthermore, the contrasting expression patterns observed in glutamate dehydrogenase (upregulation at 1.38 ≤ FC ≤ 1.63 with high Mo dose, and downregulation at 0.13 ≤ FC ≤ 0.54 with low Mo dose) and its consequential impact on glutamine expression (7.67 ≤ FC ≤ 39.60 with high Mo dose and 1.50 ≤ FC ≤ 1.95 with low Mo dose) following Mo root exposure highlighted dose-dependent regulatory trends influencing proteins and metabolites. These findings offer a multidimensional understanding of plant responses to ENMs exposure, guiding agricultural practices and environmental safety protocols while advancing knowledge on nanomaterial impacts on plant biology.

Figure 1

Partial least squares-discriminant analysis (PLS-DA) of metabolite concentrations in each plant tissue with different treatments and exposure routes.

Figure 2

Volcano plots to visualize the relationship between significance (p-values < 0.05) and fold changes (FC) for each treatment. Gray points: not significant; red points: significant and FC ≥ 1.25; blue color points: FC ≤ 0.75.

Figure 3

Heatmap of (A) 58 responsive metabolite concentrations in different plant tissues with Cu treatments. (B) 71 responsive metabolites concentrations in different plant tissues with Mo treatments. L1: leaf #1; L2: leaf #2; L3: leaf #3; S: stem; R: root; RC: root exposure control; LC: leaf exposure control; RCu: Cu exposure through root; LCu: Cu exposure through leaf; RMo: Mo exposure through root; LMo: Mo exposure through leaf. *: only responsive through root exposure; •: only responsive through leaf exposure.

Figure 4

Venn diagram of (a) responsive metabolites with Cu and Mo exposure through root and leaf; (b) tissue-specific distribution of responsive metabolites with Cu exposure through root; (c) tissue-specific distribution of responsive metabolites with Cu exposure through leaf; (d) tissue-specific distribution of responsive metabolites with Mo exposure through root; and (e) tissue-specific distribution of responsive metabolites with Mo exposure through leaf.

Figure 5

Fold change bar plots of 69 responsive metabolites (grouped by metabolite classes) in different plant tissues with Mo exposure through root. Metabolites highlighted with red squares are the ones responsive across all tissues.

Figure 6

Venn diagram of (a) responsive proteins with Cu and Mo exposure through root and leaf; (b) tissue-specific distribution of responsive proteins with Cu exposure through root; (c) tissue-specific distribution of responsive proteins with Cu exposure through leaf; (d) tissue-specific distribution of responsive proteins with Mo exposure through root; and (e) tissue-specific distribution of responsive proteins with Mo exposure through leaf.

Figure 7

Venn diagrams of (a) perturbed pathways with Cu and Mo exposure through root and leaf; (b) tissue-specific distribution of perturbed pathways with Cu exposure through root; (c) tissue-specific distribution of responsive metabolites with Cu exposure through leaf; (d) tissue-specific distribution of perturbed pathways with Mo exposure through root; and (e) tissue-specific distribution of perturbed pathways with Mo exposure through leaf.

Figure 8

Pathway mapping of responsive metabolites and proteins based on KEGG.