Metabolomic Profiling Reveals the Effects of Cu-Ag Nanoparticles on Tomato Bacterial Wilt

Abstract

Background: The bacterial wilt of tomatoes, caused by Ralstonia solanacearum, is a soil-borne plant disease that causes substantial agricultural economic losses. Various nanoparticles have been utilized as antibacterial agents to mitigate pathogenic destructiveness and improve crop yields. However, there is a lack of in-depth research on how nanoparticles affect tomato metabolite levels to regulate the bacterial wilt of tomatoes. Methods: In this study, healthy and bacterial wilt-infected tomatoes were treated with Cu-Ag nanoparticles, and a metabolomics analysis was carried out. Results: The results showed that Cu-Ag nanoparticles had a significant prevention and control effect on the bacterial wilt of tomatoes. Metabolomic analysis revealed that the nanoparticles could significantly up-regulate the expression levels of terpenol lipids, organic acids, and organic oxygen compounds in diseased tomatoes, and enhance key metabolic pathways such as amino acid metabolism, carbohydrate metabolism, secondary metabolite metabolism, and lipid metabolism. These identified metabolites and pathways could regulate plant growth and defense against pathogens. Correlation analysis between the tomato microbiome and metabolites showed that most endophytic microorganisms and rhizospheric bacteria were positively correlated with fatty acyls groups and organic oxygen compounds. Conclusions: This study reveals that Cu-Ag nanoparticles can actively regulate the bacterial wilt of tomatoes by up-regulating the levels of lipid metabolism and organic oxygen compounds, providing an important theoretical basis for the application of nanoparticles in agriculture.

Keywords: Cu-Ag nanoparticles; fatty acyls; metabolomics; organooxygen compounds; tomato.

Figure 1

Scores plot of partial least squares discriminant analysis (PLS-DA) of metabolites in all tomato samples (n = 5) (A). Principal component analysis (PCA) of root metabolites. D6 vs. D9 (B). D6 vs. D7 (C). D6 vs. D8 (D). D7 vs. D8 (E). Abbreviations: control infected tomatoes (D6); infected tomato roots treated with Cu-Ag nanoparticles (D7); infected tomato roots treated with thiodiazole-copper (D8); control healthy tomatoes (D9); healthy tomatoes treated with Cu-Ag nanoparticles (D10); and healthy tomatoes treated with thiodiazole-copper (D11).

Figure 2

Hierarchical cluster analysis of the top 50 differentially expressed metabolites in infected tomato roots (A) and healthy tomato roots (B). Each column represents a root sample, and each row indicates a differentially expressed metabolite. The color gradient transitioning from blue to red represents the varying abundance of differentially expressed metabolites, from low to high levels.

Figure 3

Analysis of the up-regulated and down-regulated metabolites in tomato roots using Lollipop Map. D9 vs. D6 (A). D7 vs. D6 (B). D8 vs. D6 (C). D8 vs. D7 (D). ** p ≤ 0.01 and *** p ≤ 0.001.

Figure 4

KEGG enrichment analysis of the pathways in infected tomato roots. The X-axis represented the route name, while the Y-axis denoted the enrichment score. The bubble’s size reflects the number of involved DEMs. D9 vs. D6 (A). D7 vs. D6 (B). D8 vs. D6 (C). D8 vs. D7 (D).

Figure 5

Analysis of endophytic microbiota (A) and metabolomic (B) characteristics in D7 vs. D6. The notable connections were determined based on the p value, and the frequency of each disparity in each group was individually assessed. The count reflects the frequency of associated items, arranged in decreasing order. Hierarchical cluster analysis of the correlation between metabolites and endophytic microbial communities (C). The horizontal axis shows bacterial communities at the phylum level, while the vertical axis illustrates the metabolic products. * p ≤ 0.05; ** p ≤ 0.01; and *** p ≤ 0.001.

Figure 6

Comprehensive analysis of the rhizosphere microbiome at the phylum level and metabolomic profiles in D7 vs. D6. Correlation plot showing the association among components (A). The top right graph illustrates the distribution of metabolite samples. The bottom left graphic illustrates the correlation coefficient of the sample distribution. Analysis of the network relationship (B). The highest correlations between metabolomics and the microbiota were identified. Squares represent metabolites, while circles indicate microbes. The color of the line reflects the correlation coefficient. Hierarchical cluster analysis of the connection between metabolites and microorganisms (C). The horizontal axis represents the microorganisms, and the vertical axis shows metabolites. Red denotes a positive connection, while blue signifies a negative correlation. The intensity of color reflects the degree of association. * p ≤ 0.05; ** p ≤ 0.01; and *** p ≤ 0.001.