Pine trees, globally distributed and economically vital evergreen conifers, are threatened by pine wilt disease (PWD) attributed to the pine wood nematode (PWN). Many studies have been conducted on phenome and transcriptome profiling in select Pinus species upon PWN infection, but a high-throughput phenotyping of PWD progression and transcriptomic analysis across diverse Pinus species remains lacking. Here, we developed a deep learning-based phenotyping program to quantify PWD symptoms and conducted a pan-transcriptome analysis using PWD-susceptible ( Pinus densiflora , Pinus koraiensis , Pinus thunbergii ) and -resistant ( Pinus parviflora , Pinus strobus , Pinus rigida × Pinus taeda ) Pinus species and a hybrid. Our results showed severe wilting of leaves within 14 weeks after PWN infection in susceptible species but not in resistant ones. Pan-transcriptomic analysis revealed the upregulation of genes involved in leaf abscission and abscisic acid responses in PWD-resistant taxa, while PWD-susceptible taxa downregulated genes associated with desiccation response after PWN infection. These findings suggest that activating genes involved in water conservation plays a role in mitigating PWD infection in Pinus trees. Notably, all five Pinus species and one hybrid exhibited upregulation of the elongation factor Tu receptor ( EFR ) gene and pathogenesis-related (PR)-3 gene upon PWN infection, suggesting a potential role of the EF-Tu receptor in detecting PWN invasion and activating the PR-3 gene. Our study introduces a novel deep learning-based phenotyping program for precise PWD symptom quantification and enhances understanding of the molecular mechanisms underlying PWD resistance. These insights contribute to high-throughput monitoring of PWD progression in Pinus forests for disease prevention and facilitate the development of PWD-resistant pine trees.
Choi et al. (Wed,) studied this question.