P-117 - Comparative Pan-Genomics of Multiple Crop Pathogen Species to Study Virulence Genes

Wednesday, July 16, 2025

13:30 - 15:15

Location: Confex Hall 3

Effector Interference Mechanisms

Presenting Author(s)

NG

Naomi Gray

Centre for Crop and Disease Management (CCDM), Curtin University
Bentley, Western Australia, Australia

Author(s)

MH

Mohitul Hossain

Centre for Crop and Disease Management (CCDM), Curtin University
Bentley, Western Australia, Australia
PM

Pavel Misiun

Centre for Crop and Disease Management (CCDM), Curtin University
Bentley, Western Australia, Australia
JD

Johannes Debler

Centre for Crop and Disease Management (CCDM), Curtin University
Bentley, Western Australia, Australia
BH

Bernadette Henares

Centre For Crop And Disease Management
Bentley, Western Australia, Australia
MS

Manisha Shankar

Department of Primary Industries and Regional Development (DPIRD)
Perth, Western Australia, Australia
HG

Hossein Golzar

Department of Primary Industries and Regional Development (DPIRD)
Perth, Western Australia, Australia
EP

Elzette Palmiero

Centre for Crop and Disease Management (CCDM), Curtin University
Bentley, Western Australia, Australia
SE

Simon Ellwood

Curtin University
Perth, Western Australia, Australia
PS

Pao Theen See

Curtin University
Perth, Western Australia, Australia
JH

James Hane

Centre for Crop and Disease Management (CCDM), Curtin University
Bentley, Western Australia, Australia

Effective prediction of fungal effector genes is an important aspect of crop disease management and surveillance. Our recently developed bioinformatics pipeline designed to mine fungal pan-genomes for pathogenicity data called MycoProcessor (with the tool EffectorFisher), can predict effector-like candidates with cultivar-specific association. This requires corresponding sequencing and phenotype data for large numbers of isolates which is not currently available for most species. However, with the quantity of fungal genome sequencing currently available we have an opportunity to use comparative pan-genomics to identify effectors and other virulence genes with broader specificity at the host level. Our dataset contains a range of pathogens that include closely related species with differing host ranges and pathogenic lifestyles. These include: Pyrenophora spp. (P. tritici-repentis (n=112), P. teres f. sp. teres (n=401) and P. teres f. sp. maculata (n=57)) and Parastagonospora nodorum (n=650), collectively infecting wheat and barley hosts; Ascochyta fabae (n=70) and A. lentis (n=150) infecting faba bean and lentil, respectively; Rhizoctonia solani AG8 (n=43) infecting wheat and lupin; Colletotrichum lupini (n=44) and Diaporthe toxica (n=36) infecting lupin. In addition to predicting effectors, we can also mine fungicide-resistance mutations and examine rates of repeat-induced point mutation, lateral gene transfer, and mesosynteny across this diverse set of pan-genomes.