Literature DB >> 28168930

Avirulence (AVR) Gene-Based Diagnosis Complements Existing Pathogen Surveillance Tools for Effective Deployment of Resistance (R) Genes Against Rice Blast Disease.

S M Selisana1, M J Yanoria1, B Quime1, C Chaipanya1, G Lu1, R Opulencia1, G-L Wang1, T Mitchell1, J Correll1, N J Talbot1, H Leung1, B Zhou1.   

Abstract

Avirulence (AVR) genes in Magnaporthe oryzae, the fungal pathogen that causes the devastating rice blast disease, have been documented to be major targets subject to mutations to avoid recognition by resistance (R) genes. In this study, an AVR-gene-based diagnosis tool for determining the virulence spectrum of a rice blast pathogen population was developed and validated. A set of 77 single-spore field isolates was subjected to pathotype analysis using differential lines, each containing a single R gene, and classified into 20 virulent pathotypes, except for 4 isolates that lost pathogenicity. In all, 10 differential lines showed low frequency (<24%) of resistance whereas 8 lines showed a high frequency (>95%), inferring the effectiveness of R genes present in the respective differential lines. In addition, the haplotypes of seven AVR genes were determined by polymerase chain reaction amplification and sequencing, if applicable. The calculated frequency of different AVR genes displayed significant variations in the population. AVRPiz-t and AVR-Pii were detected in 100 and 84.9% of the isolates, respectively. Five AVR genes such as AVR-Pik-D (20.5%) and AVR-Pik-E (1.4%), AVRPiz-t (2.7%), AVR-Pita (0%), AVR-Pia (0%), and AVR1-CO39 (0%) displayed low or even zero frequency. The frequency of AVR genes correlated almost perfectly with the resistance frequency of the cognate R genes in differential lines, except for International Rice Research Institute-bred blast-resistant lines IRBLzt-T, IRBLta-K1, and IRBLkp-K60. Both genetic analysis and molecular marker validation revealed an additional R gene, most likely Pi19 or its allele, in these three differential lines. This can explain the spuriously higher resistance frequency of each target R gene based on conventional pathotyping. This study demonstrates that AVR-gene-based diagnosis provides a precise, R-gene-specific, and differential line-free assessment method that can be used for determining the virulence spectrum of a rice blast pathogen population and for predicting the effectiveness of target R genes in rice varieties.

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Year:  2017        PMID: 28168930     DOI: 10.1094/PHYTO-12-16-0451-R

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  5 in total

1.  Dissection of broad-spectrum resistance of the Thai rice variety Jao Hom Nin conferred by two resistance genes against rice blast.

Authors:  Chaivarakun Chaipanya; Mary Jeanie Telebanco-Yanoria; Berlaine Quime; Apinya Longya; Siripar Korinsak; Siriporn Korinsak; Theerayut Toojinda; Apichart Vanavichit; Chatchawan Jantasuriyarat; Bo Zhou
Journal:  Rice (N Y)       Date:  2017-05-11       Impact factor: 4.783

2.  Pi5 and Pii Paired NLRs Are Functionally Exchangeable and Confer Similar Disease Resistance Specificity.

Authors:  Kieu Thi Xuan Vo; Sang-Kyu Lee; Morgan K Halane; Min-Young Song; Trung Viet Hoang; Chi-Yeol Kim; Sook-Young Park; Junhyun Jeon; Sun Tae Kim; Kee Hoon Sohn; Jong-Seong Jeon
Journal:  Mol Cells       Date:  2019-09-30       Impact factor: 5.034

3.  Prevalence of Ineffective Haplotypes at the Rice Blast Resistance (R) Gene Loci in Chinese Elite Hybrid Rice Varieties Revealed by Sequence-Based Molecular Diagnosis.

Authors:  Gui Xiao; Jianyuan Yang; Xiaoyuan Zhu; Jun Wu; Bo Zhou
Journal:  Rice (N Y)       Date:  2020-01-30       Impact factor: 4.783

Review 4.  From Player to Pawn: Viral Avirulence Factors Involved in Plant Immunity.

Authors:  Changjun Huang
Journal:  Viruses       Date:  2021-04-16       Impact factor: 5.048

5.  Loss and Natural Variations of Blast Fungal Avirulence Genes Breakdown Rice Resistance Genes in the Sichuan Basin of China.

Authors:  Zi-Jin Hu; Yan-Yan Huang; Xiao-Yu Lin; Hui Feng; Shi-Xin Zhou; Ying Xie; Xin-Xian Liu; Chen Liu; Ru-Meng Zhao; Wen-Sheng Zhao; Chuan-Hong Feng; Mei Pu; Yun-Peng Ji; Xiao-Hong Hu; Guo-Bang Li; Jing-Hao Zhao; Zhi-Xue Zhao; He Wang; Ji-Wei Zhang; Jing Fan; Yan Li; Yun-Liang Peng; Min He; De-Qiang Li; Fu Huang; You-Liang Peng; Wen-Ming Wang
Journal:  Front Plant Sci       Date:  2022-04-12       Impact factor: 6.627

  5 in total

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