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Meta-Analysis
. 2012;7(7):e40526.
doi: 10.1371/journal.pone.0040526. Epub 2012 Jul 12.

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

Guillermo Rodrigo  1 , Javier Carrera, Virgina Ruiz-Ferrer, Francisco J del Toro, César Llave, Olivier Voinnet, Santiago F Elena
Affiliations
Meta-Analysis

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

Guillermo Rodrigo et al. PLoS One. 2012.

Abstract

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Overview of the Systems Biology approach we followed to study the viral infection in plants.
We considered A. thaliana as model host. Microarray data from several infection experiments with viruses were collected to analyze the differentially expressed genes, and to perform functional analyses by harnessing GO annotations. In addition, by taking advantage of large databases of expression profiles derived from transcriptional perturbations, the global regulatory network of the host could be as a first approach unveiled by applying learning algorithms. The differential expression was then contextualized within the inferred network.
Figure 2
Figure 2. Phylogenetic relationships among viruses explain the similarities in gene expression.
(A) Neighbor-joining dendrogram constructed using the similarity matrix computed from the lists of differentially expressed genes. Bootstrap support values are reported next to each node. (B) Maximum-likelihood phylogenetic tree constructed from the replicase genes of the seven RNA viruses included in the study. For CaLCuV, the Rep (replicase-associated protein) was used instead. The statistical quality of the different clusters was evaluated by bootstrap. Significance levels are shown next to each node.
Figure 3
Figure 3. Functional analysis.
(A) Over- and (B) under-expressed VRFs representing biological processes. In pallid red, VRFs present in at least five of the total eight viral infections (unspecific viral response); in pallid blue, VRFs in at least three of the four potyviral infections; in pallid green, VRFs in at least three of the four Brassica-infecting viral infections; in pallid yellow, common VRFs for Potyvirus and Brassica-infecting viruses.
Figure 4
Figure 4. Outgoing connectivity distributions.
Distributions are contextualized in the TRN, for the VRGs (red) and the whole interactome (blue).
Figure 5
Figure 5. Connectivity distributions.
Distributions are contextualized in the PPIN, for the VRGs (red) and the whole interactome (blue).
Figure 6
Figure 6. Measures of subnetwork organization.
(A, B) Clustering (C) and (C, D) modularity (M) coefficients for the subnetworks generated by the VRGs, contextualized in the TRN and PPIN. Rand indicates the average value for random subnetworks (100 replicates). NS denotes non-significant value following a one-tailed z-test. Horizontal dashed lines represent the cutoff value for statistical significance.
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