Universal gene co-expression network reveals receptor-like protein genes involved in broad-spectrum resistance in pepper (Capsicum annuum L.)

Cited 11 time in scopus
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Title
Universal gene co-expression network reveals receptor-like protein genes involved in broad-spectrum resistance in pepper (Capsicum annuum L.)
Author(s)
W H Kang; J Lee; Namjin Koo; J S Kwon; B Park; Yong Min Kim; S I Yeom
Bibliographic Citation
Horticulture Research, vol. 9, pp. uhab003-uhab003
Publication Year
2022
Abstract
Receptor-like proteins (RLPs) on plant cells have been implicated in immune responses and developmental processes. Although hundreds of RLP genes have been identified in plants, only a few RLPs have been functionally characterized in a limited number of plant species. Here, we identified RLPs in the pepper (Capsicum annuum) genome and performed comparative transcriptomics coupled with the analysis of conserved gene co-expression networks (GCNs) to reveal the role of core RLP regulators in pepper-pathogen interactions. A total of 102 RNA-seq datasets of pepper plants infected with four pathogens were used to construct CaRLP-targeted GCNs (CaRLP-GCNs). Resistance-responsive CaRLP-GCNs were merged to construct a universal GCN. Fourteen hub CaRLPs, tightly connected with defense-related gene clusters, were identified in eight modules. Based on the CaRLP-GCNs, we evaluated whether hub CaRLPs in the universal GCN are involved in the biotic stress response. Of the nine hub CaRLPs tested by virus-induced gene silencing, three genes (CaRLP264, CaRLP277, and CaRLP351) showed defense suppression with less hypersensitive response-like cell death in race-specific and non-host resistance response to viruses and bacteria, respectively, and consistently enhanced susceptibility to Ralstonia solanacearum and/or Phytophthora capsici. These data suggest that key CaRLPs are involved in the defense response to multiple biotic stresses and can be used to engineer a plant with broad-spectrum resistance. Together, our data show that generating a universal GCN using comprehensive transcriptome datasets can provide important clues to uncover genes involved in various biological processes.
ISSN
2662-6810
Publisher
Oxford Univ Press
DOI
http://dx.doi.org/10.1093/hr/uhab003
Type
Article
Appears in Collections:
Division of Research on National Challenges > Plant Systems Engineering Research > 1. Journal Articles
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