Comparative analysis of microarray data in Arabidopsis transcriptome during compatible interactions with plant viruses

Abstract

BackgroundAt the moment, there are a number of publications describing gene expression profiling in virus-infected plants. Most of the data are limited to specific host-pathogen interactions involving a given virus and a model host plant – usually Arabidopsis thaliana. Even though several summarizing attempts have been made, a general picture of gene expression changes in susceptible virus-host interactions is lacking.MethodsTo analyze transcriptome response to virus infection, we have assembled currently available microarray data on changes in gene expression levels in compatible Arabidopsis-virus interactions. We used the mean r (Pearson’s correlation coefficient) for neighboring pairs to estimate pairwise local similarity in expression in the Arabidopsis genome.ResultsHere we provide a functional classification of genes with altered expression levels. We also demonstrate that responsive genes may be grouped or clustered based on their co-expression pattern and chromosomal location.ConclusionsIn summary, we found that there is a greater variety of upregulated genes in the course of viral pathogenesis as compared to repressed genes. Distribution of the responsive genes in combined viral databases differed from that of the whole Arabidopsis genome, thus underlining a role of the specific biological processes in common mechanisms of general resistance against viruses and in physiological/cellular changes caused by infection. Using integrative platforms for the analysis of gene expression data and functional profiling, we identified overrepresented functional groups among activated and repressed genes. Each virus-host interaction is unique in terms of the genes with altered expression levels and the number of shared genes affected by all viruses is very limited. At the same time, common genes can participate in virus-, fungi- and bacteria-host interaction. According to our data, non-homologous genes that are located in close proximity to each other on the chromosomes, and whose expression profiles are modified as a result of the viral infection, occupy 12% of the genome. Among them 5% form co-expressed and co-regulated clusters.

Plant Immune Responses Against Viruses: How Does a Virus Cause Disease?

“…These results also support an existing notion that SA signaling and JA/ET signaling are antagonistic to each other in response to pathogen infection (An and Mou, 2011;Van der Does et al, 2013). In addition, several genes in biological processes, such as carbon metabolism, metabolite transport, cell wall remodeling, protein synthesis and degradation, and photosynthesis, were altered in a manner similar to those described for dicot virus interactions (Whitham et al, 2003(Whitham et al, , 2006Ascencio-Ibáñez et al, 2008;García-Marcos et al, 2009;Hanssen et al, 2011;Postnikova and Nemchinov, 2012). These include genes encoding glutathione S-transferases, cytochrome P450s, glucosyl hydrolases, heat shock proteins, ribosomal components, UPS proteins, and chlorophyll a/ b binding and photosystem-related proteins (Mandadi and Scholthof, 2012).…”

“…Paradoxically, in the molecular genetics era, critical advances in our mechanistic understanding of innate immunity have been made primarily by studying plant pathogenic bacteria and fungi and primarily using dicotyledonous (dicot) hosts. Although there have been studies with viruses such as Tobacco mosaic virus (TMV), Tomato bushy stunt virus (TBSV), Potato virus X (PVX), potyviruses, cucumoviruses, and bromoviruses, almost without exception dicotyledonous plants, primarily Nicotiana benthamiana and Arabidopsis thaliana, were used as experimental hosts (Whitham et al, 2003(Whitham et al, , 2006Ascencio-Ibáñez et al, 2008;García-Marcos et al, 2009;Hanssen et al, 2011;Postnikova and Nemchinov, 2012). Far fewer studies pertain to viruses that infect grasses (the Poaceae).…”

The ROS Wheel: Refining ROS Transcriptional Footprints

“…It should be noted that the tolerant threshold used to define DEGs (P , 0.01) offers more discriminative power between studies. This similarity measure had been employed previously to identify common Arabidopsis transcriptomic responses to different viruses (Postnikova and Nemchinov, 2012). We used the respective dissimilarity (1 -DSC) directly as a distance measure for hierarchical clustering (see "Materials and Methods").…”

Characterization of a Viral Synergism in the MonocotBrachypodium distachyonReveals Distinctly Altered Host Molecular Processes Associated with Disease

“…Model plant species, such as Arabidopsis (Arabidopsis thaliana) and Nicotiana benthamiana, have been crucial in determining the mechanics of antiviral defenses. Genome-wide expression studies in these model plants identified networks of genes affected commonly by several plant viruses, whereby diverse DNA and RNA viruses alter genes in primary and secondary metabolism, detoxification, cell wall remodeling, and defense responses (Whitham et al, 2003(Whitham et al, , 2006Ascencio-Ibáñez et al, 2008;García-Marcos et al, 2009;Elena et al, 2011;Hanssen et al, 2011;Postnikova and Nemchinov, 2012). At the cellular level, specific recognition of viral nucleic acids or viral proteins by host resistance gene products inhibits viral replication or accumulation (Kohm et al, 1993;Bendahmane et al, 1999;Ishibashi et al, 2007).…”