A Comprehensive Analysis of the Transcriptomes of <i>Marssonina brunnea</i> and Infected Poplar Leaves to Capture Vital Events in Host-Pathogen Interactions

Background Understanding host-pathogen interaction mechanisms helps to elucidate the entire infection process and focus on important events, and it is a promising approach for improvement of disease control and selection of treatment strategy. Time-course host-pathogen transcriptome analyses and network inference have been applied to unravel the direct or indirect relationships of gene expression alterations. However, time series analyses can suffer from absent time points due to technical problems such as RNA degradation, which limits the application of algorithms that require strict sequential sampling. Here, we introduce an efficient method using independence test to infer an independent network that is exclusively concerned with the frequency of gene expression changes. Results Highly resistant NL895 poplar leaves and weakly resistant NL214 leaves were infected with highly active and weakly active Marssonina brunnea, respectively, and were harvested at different time points. The independent network inference illustrated the top 1,000 vital fungus-poplar relationships, which contained 768 fungal genes and 54 poplar genes. These genes could be classified into three categories: a fungal gene surrounded by many poplar genes; a poplar gene connected to many fungal genes; and other genes (possessing low degrees of connectivity). Notably, the fungal gene M6_08342 (a metalloprotease) was connected to 10 poplar genes, particularly including two disease-resistance genes. These core genes, which are surrounded by other genes, may be of particular importance in complicated infection processes and worthy of further investigation. Conclusions We provide a clear framework of the interaction network and identify a number of candidate key effectors in this process, which might assist in functional tests, resistant clone selection, and disease control in the future.背景 阐明宿主-病原体（host-pathogen）互作机制，有助于解析完整的侵染过程并聚焦关键事件，同时也是优化病害防控策略、筛选治疗方案的极具潜力的途径。基于时间序列的宿主-病原体转录组分析（transcriptome analyses）与网络推断（network inference），已被用于揭示基因表达变化的直接与间接关联。然而，由于RNA降解等技术问题，时间序列分析可能存在时间点缺失的情况，这限制了对严格时序采样有要求的算法的应用。本文介绍了一种基于独立性检验（independence test）的高效方法，可用于构建仅关注基因表达变化频率的独立网络。 结果 将高抗NL895杨树叶片与弱抗NL214杨树叶片分别接种高活性与弱活性的褐生盘二孢（Marssonina brunnea），并于不同时间点采集样本。通过独立网络推断，得到了排名前1000的关键真菌-杨树互作关系，其中包含768个真菌基因与54个杨树基因。这些基因可分为三类：被众多杨树基因围绕的真菌基因、与众多真菌基因相连的杨树基因，以及其他连接度较低的基因。值得注意的是，真菌基因M6_08342（金属蛋白酶）与10个杨树基因存在关联，其中尤其包含2个抗病基因。这类被其他基因围绕的核心基因，在复杂的侵染过程中可能发挥关键作用，值得进一步研究。 结论 本研究构建了清晰的互作网络框架，并鉴定出该过程中多个潜在关键效应因子，未来可辅助开展功能验证、抗病株系筛选以及病害防控相关工作。