Integrative meta-transcriptomics and machine learning reveal defence networks and regulatory hubs in response to tomato bacterial pathogens
Tomato (Solanum lycopersicum) is an economically important vegetable crop, however, its production is severely affected by bacterial pathogens, including Clavibacter michiganensis subsp. michiganensis (Cmm) and Pseudomonas syringae. Plant defence responses are regulated by complex hormonal networks, particularly salicylic acid and jasmonic acid, which activate partially overlapping and context-dependent signalling pathways. Although numerous transcriptomic studies have investigated hormone- and pathogen-associated responses, conserved transcriptional signatures underlying tomato defence remain incompletely understood. To address this, we performed an integrative meta-analysis of massive RNA-seq data using five complementary approaches (fixed-effects, random-effects, Rank Product, Stouffer’s Z-score, and Fisher’s method), identifying 178 upregulated and 177 downregulated consensus DEGs representing a core hormone-responsive signature. Functional enrichment analysis revealed extensive me
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