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

• Published in: PloS one Vol. 10; no. 7; p. e0134246
• Main Authors: Chen, Chengwen, Yao, Ye, Zhang, Liang, Xu, Minjie, Jiang, Jianping, Dou, Tonghai, Lin, Wei, Zhao, Guoping, Huang, Minren, Zhou, Yan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29.07.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Ascomycota - genetics; Ascomycota - pathogenicity; Biodegradation; Disease control; Disease resistance; Fungi; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Fungal; Gene Expression Regulation, Plant; Gene Regulatory Networks; Genes; Genes, Fungal; Genes, Plant; Genetic aspects; Genetic engineering; Genomes; Genomics; Host-parasite relationships; Host-pathogen interactions; Host-Pathogen Interactions - genetics; Hypertension; Infections; Inference; Kinases; Laboratories; Leaves; Life sciences; Marssonina brunnea; Medical treatment; Metalloproteinase; Molecular biology; Pathogenic fungi; Pathogens; Plant Diseases - microbiology; Plant Leaves - microbiology; Poplar; Populus; Populus - genetics; Populus - microbiology; Principal components analysis; Proteins; Ribonucleic acid; RNA; Sequential sampling; Studies; Test procedures; Time series; Transcriptome
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0134246

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. 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. 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.