bioRxivPlant BiologyPreprint
Mating-compatibility genes employed as diagnostic markers to identify novel incursions of the myrtle rust pathogen Austropuccinia psidii
J. Feng, A. Bird, Z. Luo, R. Tam, L. Shepherd, L. Murphy, L. Singh, A. Graetz, M. Moeller, L. Amorim, N. S. Massola Junior, M. A. Prodhan, L. Shuey, D. Beattie, A. T. Gonzalez, P. A. Tobias, A. Padovan, R. Kimber, A. McTaggart, M. Kehoe, B. Schwessinger, T. R. Boufleur
Abstract
Austropuccinia psidii is the causal agent of myrtle rust in over 480 species within the family Myrtaceae. Lineages of A. psidii are structured by host in its native range, and some have success on new-encounter hosts. For example, the pandemic biotype has spread beyond South America, and proliferation of other lineages is an additional risk to biodiversity and industries. Efforts to manage A. psidii incursions, including lineage differentiation, relies on variable microsatellite markers. Testing these markers is time-consuming and complex, particularly on a large scale. We designed a novel diagnostic approach targeting the fungal mating-type HD (homeodomain) transcription factor locus to address these limitations. The HD locus (bW1/2-HD1 and bE1/2-HD2) is highly polymorphic, facilitating clear biological predictions about its inheritance from founding populations. To be considered the same lineage, all four HD alleles must be identical. Our lineage diagnostics relies on PCR amplification of the HD locus in different genotypes of A. psidii followed by amplicon sequencing using Oxford Nanopore Technologies (ONT) and comparative analysis. The lineage-specific assay was validated on four isolates with existing genomes, uncharacterized isolates, and directly from infected leaf material. We reconstructed HD alleles from amplicons and confirmed their sequence identity relative to their reference. Genealogies using HD alleles confirmed the variations at the HD loci among lineages/isolates. Our study establishes a robust diagnostic tool, for differentiating known lineages of A. psidii based biological predictions. This tool holds promise for detecting new pathogen incursions and can be refined for broader applications, including air-sample detection and mixed-isolate infections.
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