bioRxivPlant BiologyPreprint
Elevated CO2 enhances tomato tolerance to Botrytis cinerea through transcriptional and metabolic defence reprogramming
Francesca Baistrocchi, Marta Orero-Bayo, Lili Yu, Victoria PastoriD, Rosa Sánchez‐LucasiD, Antoni Garcia‐MolinaiD
Abstract
Atmospheric CO2 concentration is projected to rise substantially over the coming decades, yet its impact on the molecular mechanisms governing plant immunity remains poorly understood. Here, we investigated how elevated CO2 (eCO2; 650 ppm) combined with increased temperature (+5 {degrees}C) influences tomato responses to Botrytis cinerea through integrated phenotypic, metabolomic, transcriptomic, and gene regulatory network (GRN) analyses across eight cultivars. Although cultivars displayed contrasting susceptibility under ambient conditions, eCO2 consistently enhanced tolerance across all genetic backgrounds. Multi-omics analyses revealed a partial uncoupling between transcriptional and metabolic responses during infection, with repression of photosynthesis- and carbon metabolism- related genes contrasting with the accumulation of carbon- and amino acid-derived metabolites. Under eCO2, this metabolic disruption was attenuated, preserving metabolic homeostasis during infection. GRN reconstruction identified a conserved WRKY-ERF regulatory module underlying the growth-defence trade-off, while functional perturbation demonstrated that its contribution to resistance depends on both genotype and environmental context, highlighting the importance of basal defence mechanisms. Targeted metabolomics further revealed that eCO2 promotes a metabolically primed state characterized by reinforcement of structural and chemical defence barriers rather than stronger activation of inducible immune responses. Together, our findings show that enhanced tolerance under eCO2 emerges from coordinated reorganization across regulatory and metabolic networks, providing a systems- level framework for understanding plant immunity and improving crop resilience under future climate scenarios.
- organism: plant (other)
- assay: metabolomics
- assay: bulk RNA-seq
- assay: multiome
- research focus: chromatin & epigenetic regulation
- research focus: transcription & RNA processing
- research focus: metabolism & bioenergetics
- research focus: cell type annotation
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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