Heterogeneity-guided RNA-seq meta-analysis reveals reproducible drought-responsive genes in tomato

Abstract Drought stress severely affects tomato (Solanum lycopersicum L.) growth, yield, and fruit quality. Although RNA-seq has identified drought-responsive genes in tomato, differences in experimental design and stress conditions can limit the reproducibility of DEG-based results. This study performed a meta-analysis of four independent public RNA-seq datasets comprising 51 tomato leaf samples to identify drought-responsive candidate genes supported across datasets. Differential gene expression data from individual datasets were integrated with heterogeneity assessment, identifying 5,681 heterogeneity-filtered drought-responsive meta-DEGs after study-coverage and heterogeneity-based filtering. Fixed- and random-effects pooled directions agreed for 5,647 genes (99.4%), and 3,294 genes remained significant at a random-effects FDR < 0.05. The prioritized candidate genes included those associated with MAPK signaling, auxin-responsive and redox regulation, phenylpropanoid, lipid, and cuticle metabolism, and proteolysis. GO enrichment of effect-size-significant meta-DEGs represented in at least three datasets identified nominally enriched terms related to protein processing, proteolysis, transport-related processes, photosynthesis, auxin response, and redox homeostasis. In ‘Micro-Tom’ plants subjected to soil-drying-induced drought stress, RT-qPCR analysis of ten representative genes revealed distinct temporal expression patterns. The drought-responsive marker genes SlPP2C3, SlAREB1, and SlNCED1 were also induced during progressive soil drying, providing molecular evidence of a response to irrigation withdrawal. These results demonstrate that RNA-seq-based meta-analysis combined with heterogeneity assessment can prioritize drought-responsive candidates in tomato and provide a candidate set for future functional studies.