Table 1_Pan-transcriptome analysis of pine wilt disease-resistant and susceptible Pinus species and a hybrid.xlsx

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.

松树作为全球分布且兼具重要经济价值的常绿针叶树，正遭受由松材线虫（pine wood nematode, PWN）引发的松材线虫病（pine wilt disease, PWD）的威胁。此前已有多项研究针对特定松属物种在感染松材线虫后的表型组（phenome）与转录组（transcriptome）特征展开分析，但目前仍缺乏针对多样松属物种的松材线虫病病程高通量表型分析以及跨物种转录组研究。本研究开发了一款基于深度学习的表型分析程序以量化松材线虫病症状，并针对感病松属物种（赤松、红松、黑松）、抗病松属物种（五针松、北美白松、刚松×火炬松杂交种）以及1个杂交种开展泛转录组（pan-transcriptome）分析。研究结果显示，感病松属物种在感染松材线虫后的14周内会出现严重的叶片萎蔫，而抗病物种则无此现象。泛转录组分析结果表明，抗病松属类群中参与叶片脱落与脱落酸（abscisic acid）响应的基因呈现上调表达；而感病类群在感染松材线虫后，与脱水响应相关的基因则呈现下调表达。上述研究结果表明，激活与水分保持相关的基因可缓解松属树木感染松材线虫病的病情。值得注意的是，全部5个松属物种与1个杂交种在感染松材线虫后，其延伸因子Tu受体（elongation factor Tu receptor, EFR）基因与病程相关蛋白3（pathogenesis-related, PR-3）基因均呈现上调表达，这提示EF-Tu受体可能参与识别松材线虫的入侵并激活PR-3基因的表达。本研究开发了一款可精准量化松材线虫病症状的新型深度学习表型分析程序，同时加深了我们对松材线虫病抗病分子机制的理解。这些研究成果可为松属林分中松材线虫病病程的高通量监测提供支撑，助力病害防控工作，并推动抗松材线虫病松树品种的培育。