Open AccessA Systems Approach for Identifying Resistance Factors to Rice stripe virus
Author(s) -
Kang-Min Kim,
Daeseok Choi,
Sang-Min Kim,
DoYeon Kwak,
Jaemyung Choi,
Seung−Chul Lee,
Bong-Choon Lee,
Daehee Hwang,
Ildoo Hwang
Publication year - 2012
Publication title -
molecular plant-microbe interactions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.565
H-Index - 153
eISSN - 1943-7706
pISSN - 0894-0282
DOI - 10.1094/mpmi-11-11-0282
Subject(s) - biology , quantitative trait locus , gene , oryza sativa , genetics , plant disease resistance , virus , gene expression profiling , virology , gene expression , computational biology
Rice stripe virus (RSV) causes disease that can severely affect the productivity of rice (Oryza sativa). Several RSV-resistant cultivars have been developed. However, host factors conferring RSV resistance in these cultivars are still elusive. Here, we present a systems approach for identifying potential rice resistance factors. We developed two near-isogenic lines (NIL), RSV-resistant NIL22 and RSV-susceptible NIL37, and performed gene expression profiling of the two lines in RSV-infected and RSV-uninfected conditions. We identified 237 differentially expressed genes (DEG) between NIL22 and NIL37. By integrating with known quantitative trait loci (QTL), we selected 11 DEG located within the RSV resistance QTL as RSV resistance factor candidates. Furthermore, we identified 417 DEG between RSV-infected and RSV-uninfected conditions. Using an interaction network-based method, we selected 20 DEG highly interacting with the two sets of DEG as RSV resistance factor candidates. Among the 31 candidates, we selected the final set of 21 potential RSV resistance factors whose differential expression was confirmed in the independent samples using real-time reverse-transcription polymerase chain reaction. Finally, we reconstructed a network model delineating potential association of the 21 selected factors with resistance-related processes. In summary, our approach, based on gene expression profiling, revealed potential host resistance factors and a network model describing their relationships with resistance-related processes, which can be further validated in detailed experiments.