Comparative analysis of microarray data in Arabidopsis transcriptome during compatible interactions with plant viruses
© Postnikova and Nemchinov; licensee BioMed Central Ltd. 2012
Received: 23 November 2011
Accepted: 23 April 2012
Published: 29 May 2012
At 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.
To 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.
Here 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.
In 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.
KeywordsArabidopsis Response to virus infection Microarray Co-expressed clusters
Viruses are among the most agriculturally important groups of plant pathogens, causing serious economic losses in many major crops by reducing yield and quality . Although viruses have relatively simple genetic structure, the detailed mechanisms of their interaction with host plants and means by which they manipulate a plant’s physiology toward their needs and trigger antiviral responses in hosts are still not well-defined [2–5]. Among the most important consequences of viral pathogenesis are changes in expression of host genes that define both the fate of the virus and the host’s survival chances. If plants are capable of efficiently fighting infections by inherited genetic tools, such as resistance (R) genes that are abundant in every plant species , they immediately initiate general resistance pathways leading to a hypersensitive response (HR). In susceptible plants lacking R genes to a specific viral pathogen, viruses induce a variety of responses to prime and elevate their infections. These include expression changes associated with cellular processes redirected by viruses for their demands and host defensive reactions to the pathogenesis . Understanding the balance and interplay between these two types of responses would bring light to poorly characterized molecular mechanisms of viral comprehensive control of host immune system and to the counteracting host signaling pathways. It will also help to explain continuous and interconnected genetic variability in viral and host populations, that is, co-evolution of plants and viruses.
At the moment, there are a number of publications describing gene expression profiling in virus-infected plants that are derived mostly from DNA microarrays. They indicate a significant impact of viral infection on a wide array of cellular processes . Usually, altered functional categories include responses to biotic and abiotic stresses, changes in basal plant metabolism, protein synthesis, developmental and photosynthetic processes [7–10].
Most of the data are limited to specific host-pathogen interactions involving a given virus and a model host plant, which usually is Arabidopsis thaliana. In spite of several efforts to summarize general changes in plant gene expression (due to viral, bacterial and fungal infections, insect attack, other biotic and abiotic stresses) having been made [3–5, 11], a general picture of gene expression changes in susceptible virus-host interactions is missing. Detailed knowledge about the groups of host genes participating in and/or responsive to viral pathogenesis may lead to new assertions on how host cells are controlled by infection, which defense and stress mechanisms are deployed, and why disease symptoms or deviation from normal in the growth of a plant are developed [1, 5].
In this work, in order to analyze transcriptome response to virus infection, we have assembled currently available microarray data on changes in gene expression levels in compatible Arabidopsis-virus interactions and attempted to create a functional classification of the genes with altered expression levels. We conclude that 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. Importantly, we also demonstrate that responsive genes may be grouped or clustered based on their co-expression pattern and chromosomal location.
Data source, microarray data
Arabidopsis expression data were obtained from Nottingham Arabidopsis Stock Centre microarray (NASC), ArrayExpress from the European Bioinformatics Institute database and the Gene Expression Omnibus database. Additional data were retrieved from supplementary material of published papers [see Additional file 1]. The data sets were log transformed (when needed) and significant genes were selected according to P < 0.05. We selected only those genes that significantly changed their expression level in response to pathogen attack by at least two-fold. Tandem duplicates were removed from the resulting profile. The total number of collected genes across all experiments was 52488. These data represent 44 experiments with 3 different types of pathogens: virus, bacteria and fungus. Among them there were 11 viruses and the total number of genes with significantly altered expression elicited by these viruses was 16816. This number included many identical genes (with the same ID) recorded in different experiments. After subtraction of the repeating genes, a list of 7639 unique genes was obtained. The same data set was used to obtain data for bacteria 17734 (11409 unique genes) and 15426 for fungi (among them 11047 unique genes).
We performed a meta-analysis of all the collected data on compatible virus-host interactions and also on the whole database representing viral, bacterial and fungal interactions with the host plant. We used tools from TAIR to search GO annotations and functionally classify Arabidopsis genes. To find over-represented functional groups among activated or repressed genes during virus-host interactions we used Babelomics 4 FatiGO  and SAE from agriGO . To visualize this data we used REViGO software .
Clustering pathogen related genes
The level of co-expression between two genes was defined as the Pearson’s correlation coefficient (r) of the expression level for these genes. To test for pairwise local similarity in expression in the Arabidopsis genome, the mean r of the expression profiles for neighboring pairs of genes was calculated .
The mean r calculated from the real data set was then compared with the mean r calculated from 1000 data sets in which the order of genes in the Arabidopsis genome was randomized. We generated the stochastic distribution using a function that generates an even distribution of stochastic numbers. The proportion of genes found in clusters and the size distribution of clusters were calculated, and the values were averaged for 1000 iterations.
Results and discussion
Broad changes in gene expression during susceptible virus-host interactions
To analyze plant response to virus infection, we have assembled currently available microarray data on changes of gene expression levels in Arabidopsis thaliana in response to infection with various plant viruses: Cabbage leaf curl virus (CalCuV), Cauliflower mosaic virus (CAMV), Cucumber mosaic virus (CMV), Lettuce mosaic virus (LMV), Plum pox virus (PPV), Turnip crinkle virus (TCV), Tobacco etch virus (TEV), Tobacco mosaic virus (TMV and TMV-Cg), Tobacco rattle virus (TRV), Turnip mosaic virus (TuMV) and Oilseed rape mosaic tobamovirus (ORMV) [7, 8, 10, 16–22].
Does the larger number of activated genes as compared to repressed genes correspond to a general trend of plant response to virus, reflecting a greater diversity of upregulated genes? In other words, based on this information, can we conclude as other authors have done , that there is a widespread induction of the host’s biological processes due to the virus infection? De facto, it depends on several conditions. First, individual databases available for different viruses differ extensively in the number of repressed or induced genes and combined analysis is greatly influenced by this ratio in the most comprehensive databases. Second, as mentioned above, the pool of upregulated genes is larger because of the greater variety of affected genes whereas the quantity of downregulated genes is limited. Otherwise stated, more diverse genes are upregulated during different virus infections whereas downregulated genes tend to be common regardless of the particular virus. Lastly, a more accurate illustration of the general status of gene expression changes can be derived from the analysis of their proportional representation in the sets of induced or repressed genes within each functional category, which is the subject of the following section.
Using TAIR’s functional categorization, we first assigned each gene to one of the three main gene ontologies (GO) - Biological Process, Cellular Components and Molecular Function, and next to a specific functional category (FC). It is important to emphasize three key points when relying on the GO terms in analyzing expression profiles: their generality, their obvious redundancy and their incompleteness. Redundant annotations and multiple descriptions of the same biological mechanisms represent special concern undermining an effort to address consistency in characterization of gene products. Still, the Gene Ontology project  currently provides the most constructive way to find functionally equivalent terms for the purpose of classifying gene product properties.
Distributions of genes in the three main GO domains for the whole genome and assembled viral database
Number of genes in genome
% of GO domain
Number of genes in virus database
% of virus database
Ratio % of virus/% of GO domain
GO Cellular Component
other cellular components
other cytoplasmic components
other intracellular components
unknown cellular components
GO Molecular Function
DNA or RNA binding
nucleic acid binding
other enzyme activity
other molecular functions
receptor binding or activity
structural molecule activity
transcription factor activity
unknown molecular functions
GO Biological Process
cell organization and biogenesis
DNA or RNA metabolism
electron transport or energy pathways
other biological processes
other cellular processes
other metabolic processes
response to abiotic or biotic stimulus
response to stress
unknown biological processes
It was essential to determine the extent of involvement of specific FC that represents groups of genes implicated in a particular biological mechanism in the host reaction to infection. Therefore we compared the distribution of genes assigned to different FC on the whole genome of Arabidopsis with the corresponding distribution within our database of genes that are involved in response to virus infection (Table 1), [see Additional file 3]. Presumably, the greater the share each category occupies in the virus database versus in the whole genome, the greater this FC participates in host response. We found that the percentage of genes covered by several categories, such as cell wall, cytosol, extracellular, ribosome, electron transport or energy pathways, was twice as much as the normal distribution in the whole genome, thus emphasizing the important role of these functions in host-viral interactions. A share of genes in the FC “response to stress” was also 1.7 times higher in the viral database as compared to the whole genome (Table 1) and[see Additional file 3].
Common responses to different viruses
To find common responses to different viruses, we compared patterns of gene response in individual susceptible interactions. In order to do this, we used the number of shared genes among every pair of viruses to compute a similarity matrix between them according to the formula S ij = 2n ij /(n i + n j ), where n i and n j are the number of genes with altered expression level belonging to the database for virus i and virus j, respectively, and n ij represents the number of genes shared between both viruses . Next, we arranged the computed data in accordance with the value of Sij; the higher this value is, the more similarity that exists between the two compared virus-host interactions.
Although the number of common genes affected by all viruses is very limited, each virus-host interaction is unique in terms of which genes have altered expression levels. Among them are several pathogenesis-related (PR) genes, albeit they seem to be specifically upregulated in response to particular viruses: CalCuV, TMV and TEV (PR1), CMV, CalCuV, TMV and TRV (PR2 and PR4), LMV and TRV (PR5), CMV and TMV (PR3).
Overall, we have found only 198 genes that frequently change their expression in response to the majority of the viruses [see Additional file 4. Those include genes participating in the defense or immune pathways as well as genes of catabolic and regulation processes. One of the most frequently induced genes in all interactions is AT5G38530 of the tryptophan biosynthetic pathway. The tryptophan pathway provides precursors for the synthesis of key secondary metabolites such as auxin, indole-3-acetic acid (IAA), and other molecules that help protect plants against pathogens and herbivores .
Proportional representation of different functional categories in the sets of induced or repressed genes
To identify overrepresented functional groups among activated or repressed genes, we subjected genes to analysis by Babelomics 4 FatiGO  and SEA from agriGO . FatiGO uses Fisher’s exact test for 2 × 2 contingency tables to scan for significant over-representation of GO terms in one set with respect to the other. Singular enrichment analysis (SEA) identifies enriched GO terms in a list of microarray probe sets or gene identifiers. Using both FatiGO and SEA ensures finding accurate, condensed biological data by comparing a query list to a background population from which it is derived . That is, such analyses predict a role of a certain biological processes in total response to infection rather than merely calculate a number of upregulated and downregulated genes.
When implemented with the assembled data set, FatiGO and SEA identified over-represented FC and subcategories among the sets of induced or repressed genes belonging to each of the main GO domains. Overrepresented in the set of repressed genes were those involved in defense response, hormone signaling (JA, ABA), response to external stimulus, photosynthesis and bioenergetics processes (encoding photosystem I and II proteins and electron transport chain) [see Additional file 5. Downregulation of these functions is presumably due to the virus overtaking host defense-related pathways and causing physiological changes associated with the disease symptoms .
Overrepresented in the set of induced genes were those participating in response to abiotic stimulus, responses to organic and inorganic substances, nitrogen component metabolic processes and protein transport (Golgi vesicle transport, protein targeting, and cytoskeletal protein binding). The latter host pathways are essential for facilitating virus intracellular movement. Another example of a biological process that was found only in the upregulated gene set is chromatin organization (histone modification). Chromatin structural features and posttranslational modifications play a crucial role in the regulation of gene expression . Epigenetic ‘marks’ generated by modifications of histones and DNA are spread over vast regions of chromosomes and can be altered in response to stress.
Assembling information from multiple sources, such as different microarray platforms, experimental conditions, stages of infection when samples were collected, etc. raises a question of the integrity of the combined data, since it is hardly possible to eliminate the “batch effect” from influencing final results. Even so, these disparities are not likely to change the biological truth. For instance, as presented in Figure 3, the values for TMV and TMV-Cg (a crucifer-infecting strain of TMV) are very similar to each other even though they were obtained by different authors using different platforms in totally different environments. To take into account some of the determining factors (such as infection stages at the time of analyses), when different genes may become activated and/or repressed, we also looked into combined microarray data on early and late responses to virus infection and analyzed it as much as statistically possible.
One of the main features of the late host response is repression of photosynthetic and energy pathways: photosystem I and II assembly, pentose-phosphate cycle, etc. (Figure 4). Chlorosis, or yellowing of normally green plant tissue, because of the disruption of chloroplast structure and function and a decreased amount of chlorophyll is often a direct result of these deficiencies. On the contrary, cellular respiration, catabolic processes, proteolysis and senescence are overrepresented in the induced gene set at the late stages of virus infection. Among the common characteristics of both the early and late responses is negative regulation of developmental processes. In essence, sets of host genes affected at the late stage of infection closely resemble the general picture of gene expression changes caused by viral pathogenesis.
Pathogenesis-related two- to four-gene clusters in the genome of Arabidopsis thaliana
We found 207 neighboring co-expressed genes which fall into 98 clusters under conditions of pathogenesis [see Additional file 6]. These clusters consist of groups of physically linked and functionally related genes (response to pathogen) that are co-expressed (correlation coefficient r ≥ 0.7) and possibly co-regulated but share no sequence homology. Although most of them were differentially expressed, 22 clusters were always upregulated and only 2 clusters were always downregulated. Among all identified clusters only two contained four genes, nine were composed of three genes, and eighty-six contained two genes (Figure 5B).
Differences between the stochastic distribution and the actual distribution revealed in this experiment were observed both in the number of genes included in clusters and in cluster size (Figure 5C). The number of two-gene and three-gene clusters in the experiment was almost 2.5 times and 4 times higher, respectively, than expected by chance. Four-gene clusters were obtained in the experiment only; clusters of this size were not predicted to form by chance. We found 16 overlapping clusters between our pathogen-response clusters and those predicted by Zhan et al. using microarray data representing 128 experimental conditions . Apparently, genes forming these clusters are broadly co-expressed in a wide range of conditions.
To find out if there are any functional relationships between locally co-expressed genes, we used TAIR’s GO for Arabidopsis. We found 7 molecular functions, which are shared for each of 8 gene pairs as well as 13 cellular components that are common for 23 gene pairs. As revealed by the AraCyc database files from the Plant Metabolic Network, none of our clusters belongs to the same pathway [see Additional file 6].
Therefore, co-expressed neighbors do not seem to be associated with a particular GO . However, it does not mean that clustered genes are not functionally related to each other. That is, in spite of belonging to different GO categories, co-expressed groups of genes are affiliated with the same function – stress response. Plants re-arrange their metabolism upon recognition of pathogen-associated molecular patterns (PAMP) so that genes of different GO categories that are involved in defense mechanisms are engaged .
Interestingly, one of the clusters includes three genes encoding leucine-rich repeat (LRR) family proteins: AT1G33590, AT1G33600 and AT1G33610. Two more genes that are absent in available microarray data sets, AT1G33612 and AT1G33670, also encode LRR proteins and are located in the same chromosomal region. In addition, we found three other clusters containing genes with common domain structure and functional characteristics: i. cluster with a Toll-Interleukin-Resistance (TIR) domain (AT1G72900 and AT1G72910); ii. cluster with genes encoding SAM superfamily proteins (S-adenosyl-L-methionine-dependent methyltransferases superfamily, AT4G00740, and AT4G00750); and iii. cluster with genes encoding histone superfamily proteins (AT4G40030 and AT4G40040).
Previously, we reported on the clustering of pathogen-response genes in the genome of Arabidopsis thaliana. That study was based on the profiling of EST databases derived from different plant species infected with fungi, bacteria, and viruses . While comparing gene clusters revealed by broad EST mining with analysis of microarray data sets specific for compatible virus-host interactions (this investigation), we found that most groups of neighboring genes determined in the former study could be included in the clusters identified in this work, providing that both up and downregulated genes derived from different experiments are counted (Figure 5A). However, if only co-expressed genes are considered (Figure 5B), overlap between these two data sets is quite low, which could possibly be explained by a unique pattern of chromosomal gene clustering characteristic for different types and/or individual pathogens.
Common genes participate in virus-, fungi- and bacteria-host interaction
As mentioned above, we tried to assemble the largest possible database of Arabidopsis genes responsive to viral infections using currently available microarray data. For comparison, we also put together genes derived from microarray experiments with bacteria and fungi. Since there is significantly more information on changes in plant gene expression due to infection with bacteria and fungi, we limited the number of genes to approximately the same number as was compiled for viruses: 17734 for bacteria (11409 unique genes) and 15426 for fungi (among them 11047 unique genes) [11, 33–36].
We have assembled currently available microarray data on changes in gene expression levels in compatible Arabidopsis-virus interactions. In 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 FC 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, which provided an in-depth view of the role of certain biological processes in response to infection. Each virus-host interaction was found to be unique in terms of the genes with altered expression levels, and the number of common genes affected by all viruses was very limited. We discovered that genes with expression profiles modified as a result of viral infection were often located in close proximity to each other on the same chromosomes forming a multiple clusters, consisting of physically linked and functionally related genes. Finally, combining genes derived from microarray experiments with bacteria and fungi with a viral data set, we observed that gene expression changes in response to all pathogens were very similar and that nearly half of the genes associated with viral infections in susceptible hosts were also involved in response to bacterial or fungal infections.
Cabbage leaf curl virus
Cauliflower mosaic virus
Cucumber mosaic virus
Lettuce mosaic virus
Plum pox virus
Turnip crinkle virus
Tobacco etch virus
- TMV and TMV-Cg:
Tobacco mosaic virus
Tobacco rattle virus
Turnip mosaic virus
Oilseed rape mosaic tobamovirus
Gene ontology annotations
This work was supported by the United States Department of Agriculture, Agricultural Research Service. We thank Wesley Schonborn for critical reading of the manuscript and language editing.
- Hull R: Matthews’ plant virology. 2002, Academic, San DiegoGoogle Scholar
- Kang BC, Yeam I, Jahn MM: Genetics of plant virus resistance. Annu Rev Phytopathol. 2005, 43: 581-621. 10.1146/annurev.phyto.43.011205.141140.PubMedView ArticleGoogle Scholar
- Whitham SA, Quan S, Chang HS, Cooper B, Estes B, Zhu T, Wang X, Hou YM: Diverse RNA viruses elicit the expression of common sets of genes in susceptible Arabidopsis thaliana plants. Plant J. 2003, 33: 271-283. 10.1046/j.1365-313X.2003.01625.x.PubMedView ArticleGoogle Scholar
- Ma S, Bohnert HJ: Integration of Arabidopsis thaliana stress-related transcript profiles, promoter structures, and cell-specific expression. Genome Biol. 2007, 8: R49-10.1186/gb-2007-8-4-r49.PubMedPubMed CentralView ArticleGoogle Scholar
- Whitham SA, Yang C, Goodin MM: Global impact: elucidating plant responses to viral infection. Mol Plant Microbe Interact. 2006, 19: 1207-1215. 10.1094/MPMI-19-1207.PubMedView ArticleGoogle Scholar
- Bakker EG, Toomajian C, Kreitman M, Bergelson J: A genome-wide survey of R gene polymorphisms in Arabidopsis. Plant Cell. 2006, 18: 1803-1818. 10.1105/tpc.106.042614.PubMedPubMed CentralView ArticleGoogle Scholar
- Golem S, Culver JN: Tobacco mosaic virus induced alterations in the gene expression profile of Arabidopsis thaliana. Mol Plant Microbe Interact. 2003, 16: 681-688. 10.1094/MPMI.2003.16.8.681.PubMedView ArticleGoogle Scholar
- Espinoza C, Medina C, Somerville S, Arce-Johnson P: Senescence-associated genes induced during compatible viral interactions with grapevine and Arabidopsis. J Exp Bot. 2007, 58: 3197-3212. 10.1093/jxb/erm165.PubMedView ArticleGoogle Scholar
- Agudelo-Romero P, Carbonell P, de la Iglesia F, Carrera J, Rodrigo G, Jaramillo A, Perez-Amador MA, Elena SF: Changes in the gene expression profile of Arabidopsis thaliana after infection with Tobacco etch virus. Virol J. 2008, 5: 92-10.1186/1743-422X-5-92.PubMedPubMed CentralView ArticleGoogle Scholar
- Babu M, Griffiths JS, Huang TS, Wang A: Altered gene expression changes in Arabidopsis leaf tissues and protoplasts in response to Plum pox virus infection. BMC Genomics. 2008, 9: 325-10.1186/1471-2164-9-325.PubMedPubMed CentralView ArticleGoogle Scholar
- De Vos M, Van Oosten VR, Van Poecke RM, Van Pelt JA, Pozo MJ, Mueller MJ, Buchala AJ, Metraux JP, Van Loon LC, Dicke M, Pieterse CM: Signal signature and transcriptome changes of Arabidopsis during pathogen and insect attack. Mol Plant Microbe Interact. 2005, 18: 923-937. 10.1094/MPMI-18-0923.PubMedView ArticleGoogle Scholar
- Medina I, Carbonell J, Pulido L, Madeira SC, Goetz S, Conesa A, Tarraga J, Pascual-Montano A, Nogales-Cadenas R, Santoyo J, et al: Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res. 2010, 38: W210-213. 10.1093/nar/gkq388.PubMedPubMed CentralView ArticleGoogle Scholar
- Du Z, Zhou X, Ling Y, Zhang Z, Su Z: agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010, 38: W64-70. 10.1093/nar/gkq310.PubMedPubMed CentralView ArticleGoogle Scholar
- Supek F, Skunca N, Repar J, Vlahovicek K, Smuc T: Translational selection is ubiquitous in prokaryotes. PLoS Genet. 2010, 6: e1001004-10.1371/journal.pgen.1001004.PubMedPubMed CentralView ArticleGoogle Scholar
- Williams EJ, Bowles DJ: Coexpression of neighboring genes in the genome of Arabidopsis thaliana. Genome Res. 2004, 14: 1060-1067. 10.1101/gr.2131104.PubMedPubMed CentralView ArticleGoogle Scholar
- Ishihara T, Sakurai N, Sekine KT, Hase S, Ikegami M, Shibata D, Takahashi H: Comparative analysis of expressed sequence tags in resistant and susceptible ecotypes of Arabidopsis thaliana infected with cucumber mosaic virus. Plant Cell Physiol. 2004, 45: 470-480. 10.1093/pcp/pch057.PubMedView ArticleGoogle Scholar
- Ascencio-Ibanez JT, Sozzani R, Lee TJ, Chu TM, Wolfinger RD, Cella R, Hanley-Bowdoin L: Global analysis of Arabidopsis gene expression uncovers a complex array of changes impacting pathogen response and cell cycle during geminivirus infection. Plant Physiol. 2008, 148: 436-454. 10.1104/pp.108.121038.PubMedPubMed CentralView ArticleGoogle Scholar
- Marathe R, Guan Z, Anandalakshmi R, Zhao H, Dinesh-Kumar SP: Study of Arabidopsis thaliana resistome in response to cucumber mosaic virus infection using whole genome microarray. Plant Mol Biol. 2004, 55: 501-520. 10.1007/s11103-004-0439-0.PubMedView ArticleGoogle Scholar
- Yu W, Murfett J, Schoelz JE: Differential induction of symptoms in Arabidopsis by P6 of Cauliflower mosaic virus. Mol Plant Microbe Interact. 2003, 16: 35-42. 10.1094/MPMI.2003.16.1.35.PubMedView ArticleGoogle Scholar
- Agudelo-Romero P, Carbonell P, Perez-Amador MA, Elena SF: Virus adaptation by manipulation of host’s gene expression. PLoS One. 2008, 3: e2397-10.1371/journal.pone.0002397.PubMedPubMed CentralView ArticleGoogle Scholar
- Dempsey DA, Pathirana MS, Wobbe KK, Klessig DF: Identification of an Arabidopsis locus required for resistance to turnip crinkle virus. Plant J. 1997, 11: 301-311. 10.1046/j.1365-313X.1997.11020301.x.PubMedView ArticleGoogle Scholar
- Yang C, Guo R, Jie F, Nettleton D, Peng J, Carr T, Yeakley JM, Fan JB, Whitham SA: Spatial analysis of arabidopsis thaliana gene expression in response to Turnip mosaic virus infection. Mol Plant Microbe Interact. 2007, 20: 358-370. 10.1094/MPMI-20-4-0358.PubMedView ArticleGoogle Scholar
- Dardick C: Comparative expression profiling of Nicotiana benthamiana leaves systemically infected with three fruit tree viruses. Mol Plant Microbe Interact. 2007, 20: 1004-1017. 10.1094/MPMI-20-8-1004.PubMedView ArticleGoogle Scholar
- [http://www.geneontology.org/GO.doc.shtml] GOp
- Niyogi KK, Fink GR: Two anthranilate synthase genes in Arabidopsis: defense-related regulation of the tryptophan pathway. Plant Cell. 1992, 4: 721-733.PubMedPubMed CentralView ArticleGoogle Scholar
- Kouzarides T: Chromatin modifications and their function. Cell. 2007, 128: 693-705. 10.1016/j.cell.2007.02.005.PubMedView ArticleGoogle Scholar
- Lee JM, Sonnhammer EL: Genomic gene clustering analysis of pathways in eukaryotes. Genome Res. 2003, 13: 875-882. 10.1101/gr.737703.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhan S, Horrocks J, Lukens LN: Islands of co-expressed neighbouring genes in Arabidopsis thaliana suggest higher-order chromosome domains. Plant J. 2006, 45: 347-357. 10.1111/j.1365-313X.2005.02619.x.PubMedView ArticleGoogle Scholar
- Mentzen WI, Wurtele ES: Regulon organization of Arabidopsis. BMC Plant Biol. 2008, 8: 99-10.1186/1471-2229-8-99.PubMedPubMed CentralView ArticleGoogle Scholar
- Jones JD, Dangl JL: The plant immune system. Nature. 2006, 444: 323-329. 10.1038/nature05286.PubMedView ArticleGoogle Scholar
- Postnikova OA MN, Boutanaev AM, Nemchinov LG: Clustering Of Pathogen-Response Genes In The Genome Of Arabidopsis Thaliana. JIPB. 2011, in pressGoogle Scholar
- Boutanaev AM, Postnikova OA, Nemchinov LG: Mapping of heterologous expressed sequence tags as an alternative to microarrays for study of defense responses in plants. BMC Genomics. 2009, 10: 273-10.1186/1471-2164-10-273.PubMedPubMed CentralView ArticleGoogle Scholar
- Bethke G, Unthan T, Uhrig JF, Poschl Y, Gust AA, Scheel D, Lee J: Flg22 regulates the release of an ethylene response factor substrate from MAP kinase 6 in Arabidopsis thaliana via ethylene signaling. Proc Natl Acad Sci U S A. 2009, 106: 8067-8072. 10.1073/pnas.0810206106.PubMedPubMed CentralView ArticleGoogle Scholar
- Ferrari S, Galletti R, Denoux C, De Lorenzo G, Ausubel FM, Dewdney J: Resistance to Botrytis cinerea induced in Arabidopsis by elicitors is independent of salicylic acid, ethylene, or jasmonate signaling but requires PHYTOALEXIN DEFICIENT3. Plant Physiol. 2007, 144: 367-379. 10.1104/pp.107.095596.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang L, Mitra RM, Hasselmann KD, Sato M, Lenarz-Wyatt L, Cohen JD, Katagiri F, Glazebrook J: The genetic network controlling the Arabidopsis transcriptional response to Pseudomonas syringae pv. maculicola: roles of major regulators and the phytotoxin coronatine. Mol Plant Microbe Interact. 2008, 21: 1408-1420. 10.1094/MPMI-21-11-1408.PubMedView ArticleGoogle Scholar
- Wang L, Tsuda K, Sato M, Cohen JD, Katagiri F, Glazebrook J: Arabidopsis CaM binding protein CBP60g contributes to MAMP-induced SA accumulation and is involved in disease resistance against Pseudomonas syringae. PLoS Pathog. 2009, 5: e1000301-10.1371/journal.ppat.1000301.PubMedPubMed CentralView ArticleGoogle Scholar
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