bioRxivPlant BiologyPreprint
Dynamic Rysto receptor remodeling controls its ability to confer extreme resistance
Marta Grech‐BaraniD, Juan Camilo OchoaiD, Teresa Vargas-CorteziD, Małgorzata LichockaiD, Izabela Barymow-FiloniukiD, Jarosław T. PoznańskiiD, Magdalena KrzymowskaiD
Abstract
Plant nucleotide-binding leucine-rich repeat receptors (NLRs) mediate effector-triggered immunity (ETI), often accompanied by a hypersensitive response (HR). Conversely, extreme resistance (ER) provides exceptionally rapid and effective antiviral protection without visible cell death, yet the molecular and physiological mechanisms underlying ER remain poorly defined. The TIR-NLR receptor Rysto recognizes the coat protein (CP) of potato virus Y (PVY) and can trigger either ER or HR depending on the context. Here, we demonstrate that the efficacy of Rysto-mediated ER relies on the orchestration of various defense mechanisms. These involve transcriptional priming, and spatial redistribution of the receptor and a subset of chaperones. Rysto-expressing lines exhibit a preactivated immune state, including increased expression of genes encoding glycine-rich proteins associated with the cell wall interface and thickness. Upon PVY infection, Rysto plants undergo rapid transcriptome reprogramming, including redox- and defense-related pathways. At the protein level, Rysto initially forms a pre-active complex with Hsp70 and CP-interacting cochaperonins (CPIPs) which is remodeled upon PVY CP binding. PVY CP competes for Hsp70, enabling Rysto to oligomerize into an active resistosome. This process is further accompanied by relocalization of a subpool of the receptor toward an interface of plasma membrane, cell wall. Our research reveals a chaperone-mediated activation mechanism and spatial immune repositioning that differentiate ER responses from traditional HR, offering a detailed system for achieving durable antiviral resistance in plants.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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