bioRxivPlant Biology
Stress Recovery Triggers Rapid Transcriptional Reprogramming and Activation of Immunity in Plants
N. Illouz-Eliaz, J. Yu, J. Swift, K. Lande, B. Jow, Z. K. Tuang, T. Lee, A. Yaaran, R. G. Castanon, J. R. Nery, T. Nobori, Y. Zait, S. Burdman, J. R. Ecker
Abstract
All organisms experience stress as an inevitable part of life, from single-celled microorganisms to complex multicellular beings. The ability to recover from stress is a fundamental trait that determines the overall resilience of an organism, yet stress recovery is understudied. To begin unraveling the stress recovery process we studies recovery from drought stress in Arabidopsis thaliana. We performed a fine-scale time series of bulk RNA sequencing starting 15 minutes after rehydration following moderate drought. We reveal that drought recovery is a rapid process involving the activation of thousands of recovery-specific genes. To capture these rapid recovery responses in different leaf cell types, we performed single-nucleus transcriptome analysis at the onset of post-drought recovery, identifying a cell type-specific transcriptional state developing within 15 minutes of rehydration independently across cell types. Furthermore, we reveal a recovery-induced activation of the immune system that occurs independent of infection, which enhances pathogen resistance in vivo in A. thaliana, wild tomato (Solanum pennellii) and domesticated tomato (Solanum lycopersicum cv. M82). Since rehydration promotes microbial proliferation and thereby increases the risk of infection1-2, drought recovery-induced immunity may be crucial for plant survival in natural environments. These findings indicate that drought recovery coincides with a preventive defense response, unraveling the complex regulatory mechanisms that facilitate stress recovery in different plant cell types.
- organism: Arabidopsis
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: host-pathogen & immune response
- approach: statistical model
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