Scientific ReportsPlant BiologyPeer-reviewed journal article
Integrating Gene Expression Analysis and Ecophysiological Responses to Water Deficit in Leaves of Tomato Plants
Giovanni BortolamiiD, Tobias de WerkiD, Maximilian LarteriD, Ajaree ThonglimiD, Bernd Mueller‐RoeberiD, Salma BalazadehiD, Frederic LensiD
Abstract
Soil water deficit (WD) is one of the most important abiotic stresses affecting plant survival and crop yield. Despite its economic relevance, many gaps remain in our understanding of how crops respond to WD, especially concerning the synergistic coordination of molecular and ecophysiological adaptations delaying plant damage and mortality. In this study, we investigated the gene expression imposed by a progressive WD and combined it with measurements pointing to key ecophysiological thresholds in leaves of tomato plants. We uncovered the transcriptomic changes in mature leaves at four stages defined by physiological markers relating to different WD intensities: partial stomatal closure, complete stomatal closure, after leaf wilting, and beginning of embolism development in the veins. By identifying key transcription factors (TFs) across these progressively worsening WD stages, we investigated the timing and impact of ABA-(in)dependent gene regulatory pathways during WD. In addition, we compared the transcriptome in young developing versus mature leaves and explored the physiological mechanisms that may explain the higher tolerance to dehydration in younger leaves. By correlating the transcriptomic changes to precise ecophysiological measurements, the combined dataset will serve as a framework for future studies comparing leaf molecular and physiological responses to WD at specific intensities. HighlightIntegrated ecophysiological and gene expression analyses identify key mechanisms underlying the different thresholds of tomato responses to water deficit
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: development & morphogenesis
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 9
- FWCI
- 1.90
- Field percentile
- top 15%
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