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Poster

Session: Session 2 Presenting Authors at Posters (Odds)

P-131 - Improving fungal effector prediction and enabling resistant cultivar selection by integrating disease phenotyping and pathogen pan-genomics

Wednesday, July 16, 2025
13:30 - 15:15  
Location: Confex Hall 3
Effector Interference Mechanisms

    Presenting Author(s)

  • MH

    Mohitul Hossain

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

    • Author(s)

  • KG

    Kristina Gagalova

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

  • NG

    Naomi Gray

    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

  • FK

    Fiona Kamphuis

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

  • LH

    Lukas Hunziker

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

  • EF

    Eiko Furuki

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

  • HG

    Hossein Golzar

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

  • MS

    Manisha Shankar

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

  • HP

    Huyen Phan

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

  • JH

    James Hane

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

Fungal effectors interact with host cognate receptors to cause disease, and their accurate identification is important for crop disease management. In the last decade, effector prediction tools based on physicochemical properties have significantly improved in accuracy. However, a gap remains between the number of predictions and feasible experimental validation, so obtaining a reliably reduced candidate list is important. EffectorFisher is a novel approach integrating protein isoform profiles derived from pathogen pan-genomic data with disease phenotyping to narrow down effector candidates. It was tested on the Parastagonospora nodorum-wheat (necrotrophic) and Zymoseptoria tritici-wheat (hemibiotrophic) pathosystems. It improved the ranking of known effectors by 2-13 times, reduced candidate numbers by up to ~3.5-fold, and predicted cultivar-specific effector interactions for both necrotroph (inverse gene-for-gene) and hemibiotroph (gene-for-gene) pathosystems. Minimum-viable experimental design was assessed using simulated datasets with reduced numbers of P. nodorum isolates and/or phenotyped cultivars, which generated comparable results for known effectors for upwards of ~40 isolates and ~4 cultivars. It also outperformed GWAS, and can provide results with non-quantitative phenotyping (i.e. cultivar-of-isolation). EffectorFisher also led to development of a new method, CultSelect, designed to predict the disease phenotype of cultivars from new pathogen sequence data.