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Plant nutrition is connected to defense against insect herbivores, but the exact mechanism underlying the effect of the nitrogen (N) supply on the anti-herbivore capacity of eggplants (Solanum melongena) has not been studied in detail. Therefore, we examined the impact of low (LN, 0.5 mM) and high (HN, 5 mM) nitrate levels on eggplant resistance against the western flower thrips Frankliniella occidentalis (WFT), a major destructive eggplant pest. Our results showed that LN plants displayed enhanced defense responses to WFT compared to HN plants. This included increased transcript levels of key genes in the jasmonic acid (JA) pathway, the accumulation of JA-amido conjugates (jasmonoyl-isoleucine, jasmonoyl-phenylalanine, and jasmonoyl-valine), JA precursor (12-oxophytodienoic acid), and methyl jasmonate, higher transcript levels of defense marker genes (MPK3, MPK7, and WRKY53), and increased activities of polyphenol oxidase and peroxidase upon a WFT attack. Our findings suggest that N deficiency can prime JA-mediated defense responses in eggplants, resulting in increased anti-herbivore resistance.

Matrix-assisted laser desorption/ionization time-of-flight mass (MALDI-TOF) spectrometry fingerprinting has reduced turnaround times, costs, and labor as conventional procedures in various laboratories. However, some species strains with high genetic correlation have not been directly distinguished using conventional standard procedures. Metabolomes can identify these strains by amplifying the minor differences because they are directly related to the phenotype. The pseudotargeted metabolomics method has the advantages of both non-targeted and targeted metabolomics. It can provide a new semi-quantitative fingerprinting with high coverage. We combined this pseudotargeted metabolomic fingerprinting with deep learning technology for the identification and visualization of the pathogen. A variational autoencoder framework was performed to identify and classify pathogenic bacteria and achieve their visualization, with prediction accuracy exceeding 99%. Therefore, this technology will be a powerful tool for rapidly and accurately identifying pathogens.