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MDPI, Genes, 9(11), p. 1107, 2020

DOI: 10.3390/genes11091107



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Integrated Analysis of Gene Expression, SNP, InDel, and CNV Identifies Candidate Avirulence Genes in Australian Isolates of the Wheat Leaf Rust Pathogen Puccinia triticina

Journal article published in 2020 by Jing Qin Wu ORCID, Long Song ORCID, Chong Mei Dong ORCID, Robert F. Park ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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The leaf rust pathogen, Puccinia triticina (Pt), threatens global wheat production. The deployment of leaf rust (Lr) resistance (R) genes in wheat varieties is often followed by the development of matching virulence in Pt due to presumed changes in avirulence (Avr) genes in Pt. Identifying such Avr genes is a crucial step to understand the mechanisms of wheat-rust interactions. This study is the first to develop and apply an integrated framework of gene expression, single nucleotide polymorphism (SNP), insertion/deletion (InDel), and copy number variation (CNV) analysis in a rust fungus and identify candidate avirulence genes. Using a long-read based de novo genome assembly of an isolate of Pt (‘Pt104′) as the reference, whole-genome resequencing data of 12 Pt pathotypes derived from three lineages Pt104, Pt53, and Pt76 were analyzed. Candidate avirulence genes were identified by correlating virulence profiles with small variants (SNP and InDel) and CNV, and RNA-seq data of an additional three Pt isolates to validate expression of genes encoding secreted proteins (SPs). Out of the annotated 29,043 genes, 2392 genes were selected as SP genes with detectable expression levels. Small variant comparisons between the isolates identified 27–40 candidates and CNV analysis identified 14–31 candidates for each Avr gene, which when combined, yielded the final 40, 64, and 69 candidates for AvrLr1, AvrLr15, and AvrLr24, respectively. Taken together, our results will facilitate future work on experimental validation and cloning of Avr genes. In addition, the integrated framework of data analysis that we have developed and reported provides a more comprehensive approach for Avr gene mining than is currently available.