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

Chen C, Yao Y, Zhang L, Xu M, Jiang J, Dou T, Lin W, Zhao G, Huang M, Zhou Y - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, People's Republic of China; Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai, Shanghai, People's Republic of China; Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Principal component analysis (PCA) of poplar genes during infection.The graph illustrates the distribution of each sample in the space of the first two principal components (PCs). The samples infected with weakly active fungi are clustered together, whereas the distribution of the highly active group is relatively scattered.
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pone.0134246.g001: Principal component analysis (PCA) of poplar genes during infection.The graph illustrates the distribution of each sample in the space of the first two principal components (PCs). The samples infected with weakly active fungi are clustered together, whereas the distribution of the highly active group is relatively scattered.

Mentions: In PCA analysis, PC1 and PC2 explained 95% of the variations in gene expression and represented the differences between these libraries (Fig 1). Notably, the samples infected with weakly active fungi were clustered, whereas the distribution of the highly active group was relatively scattered, which suggested large changes at the gene expression level during infection by highly active fungi.


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

Chen C, Yao Y, Zhang L, Xu M, Jiang J, Dou T, Lin W, Zhao G, Huang M, Zhou Y - PLoS ONE (2015)

Principal component analysis (PCA) of poplar genes during infection.The graph illustrates the distribution of each sample in the space of the first two principal components (PCs). The samples infected with weakly active fungi are clustered together, whereas the distribution of the highly active group is relatively scattered.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4519268&req=5

pone.0134246.g001: Principal component analysis (PCA) of poplar genes during infection.The graph illustrates the distribution of each sample in the space of the first two principal components (PCs). The samples infected with weakly active fungi are clustered together, whereas the distribution of the highly active group is relatively scattered.
Mentions: In PCA analysis, PC1 and PC2 explained 95% of the variations in gene expression and represented the differences between these libraries (Fig 1). Notably, the samples infected with weakly active fungi were clustered, whereas the distribution of the highly active group was relatively scattered, which suggested large changes at the gene expression level during infection by highly active fungi.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, People's Republic of China; Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai, Shanghai, People's Republic of China; Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus