Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of Arabidopsis response to bacterial-(LPS, HrpZ, Flg22) and oomycete-(NPP1) derived elicitors - AtGenExpress

ABSTRACT: AtGenExpress: A multinational coordinated effort to uncover the transcriptome of the multicellular model organism Arabidopsis thaliana (Hybridisations done at NASC); The activity of genes and their encoded products can be regulated in several ways, but transcription is the primary level, since all other modes of regulation (RNA splicing, RNA and protein stability, etc.) are dependent on a gene being transcribed in the first place. The importance of transcriptional regulation has been underscored by the recent flood of global expression analyses, which have confirmed that transcriptional co-regulation of genes that act together is the norm, not the exception. Moreover, many studies suggest that evolutionary change is driven in large part by modifications of transcriptional programs. An essential first step toward deciphering the transcriptional code is to determine the expression pattern of all genes. With this goal in mind, an international effort to develop a gene expression atlas of Arabidopsis has been underway since fall 2003. This project, dubbed AtGenExpress, is funded by the DFG, and will provide the Arabidopsis community with access to a large set of Affymetrix microarray data. As part of this collaboration, we have generated expression data from 80 biologicaly different samples in triplicate. Series 2: Base line experiment for pathogen infection. Growth and infection conditions: Seeds will be stratified for 3 days at 4Â°C and sown on soil. Plants are grown at 22Â°C under a 8/16 hour light/dark regime in Percival growth chambers. All pathogen treatments will be performed on leaves of 5-weeks old plants. Bacterial infiltrations will be performed with 10-8 cfu/ml in 10 mM MgCl2. 5x105 spores of Phytophthora infestans in water will be applied to leaf surfaces. LPS (100 Âµg/ml), HrpZ (1 ÂµM), NPP1 (2 ÂµM) or flagellin (1 ÂµM flg22) will be applied in water. Experiments should be performed in triplicate at the time points indicated below. Experimenter name = Frederic Brunner , Thorsten NÃ¼rnberger; Experimenter institute = AtGenExpress Experiment Overall Design: 42 samples were used in this experiment

ORGANISM(S): Arabidopsis thaliana

SUBMITTER: Nottingham Arabidopsis Stock Centre (NASC)

PROVIDER: E-GEOD-5615 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Expression profiles of 22 reference Arabidopsis immunity mutants were collected using the Arabidopsis Pathoarray 464_001 (GPL3638) in order to build a network model predicting the Arabidopsis immune signaling network. Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. Here, we demonstrate that use of mRNA profiling to collect and analyze detailed descriptions of changes in the network state resulting from specific network perturbations is a powerful and economical strategy to elucidate regulatory relationships among the components of a complex signaling network. Specifically, we studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with the Pto DC3000 AvrRpt2 and used as detailed descriptions of the network states resulting from specific genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting network model accurately predicted 22 of 23 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; (ii) negative regulatory relationships are common between signaling sectors. One case of a novel negative regulatory relationship, between the early microbe-associated molecular pattern (MAMP)-activated sector and the salicylic acid (SA)-mediated sector, was further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. Keywords: Responses of reference Arabidopsis immunity mutants to Pseudomonas syringae pv. tomato DC3000 carrying avrRpt2 This experiment consists of two (group00) or three (group01-04) biological replicates of each genotype (total 25 genotypes [3 are multiple mutants, which were removed for the network modeling, but were used for normalization]). For each genotype, two leaves per plant were pooled from three pots to prepare total RNA.

Project description:AtGenExpress: A multinational coordinated effort to uncover the transcriptome of the multicellular model organism Arabidopsis thaliana (Hybridisations done at NASC); The activity of genes and their encoded products can be regulated in several ways, but transcription is the primary level, since all other modes of regulation (RNA splicing, RNA and protein stability, etc.) are dependent on a gene being transcribed in the first place. The importance of transcriptional regulation has been underscored by the recent flood of global expression analyses, which have confirmed that transcriptional co-regulation of genes that act together is the norm, not the exception. Moreover, many studies suggest that evolutionary change is driven in large part by modifications of transcriptional programs. An essential first step toward deciphering the transcriptional code is to determine the expression pattern of all genes. With this goal in mind, an international effort to develop a gene expression atlas of Arabidopsis has been underway since fall 2003. This project, dubbed AtGenExpress, is funded by the DFG, and will provide the Arabidopsis community with access to a large set of Affymetrix microarray data. As part of this collaboration, we have generated expression data from 80 biologicaly different samples in triplicate. Series 2: Base line experiment for pathogen infection. Growth and infection conditions: Seeds will be stratified for 3 days at 4Â°C and sown on soil. Plants are grown at 22Â°C under a 8/16 hour light/dark regime in Percival growth chambers. All pathogen treatments will be performed on leaves of 5-weeks old plants. Bacterial infiltrations will be performed with 10-8 cfu/ml in 10 mM MgCl2. 5x105 spores of Phytophthora infestans in water will be applied to leaf surfaces. LPS (100 Âµg/ml), HrpZ (1 ÂµM), NPP1 (2 ÂµM) or flagellin (1 ÂµM flg22) will be applied in water. Experiments should be performed in triplicate at the time points indicated below. Experimenter name = Dierk Scheel , Frederic Brunner , Lore Westphal; Experimenter institute = AtGenExpress Experiment Overall Design: 18 samples were used in this experiment

Project description:Despite their importance, plant MAP kinase targets are still poorly elucidated. Here, the specific in vivo interaction of an ethylene response factor (ERF104) with the Arabidopsis MAP kinase, MPK6, is shown by fluorescence resonance energy transfer. The interaction, which is lost within minutes after treatment with the flagellin-derived flg22 peptide, is dependent on both MPK6 kinase activity and rapid ethylene signaling initiated downstream of MPK6 activation. ERF104 is an MPK6 substrate and phosphorylation site mutations affected its stability. ERF104 activates promoters with GCC elements. This was evident from microarray data of overexpressing transgenic plants, where promoters of up regulated genes contain GCC motifs and chromatin immunoprecipitation showing ERF104 association with PDF1.2 promoter. The ERF104 overexpressor did not affect biotrophic bacteria proliferation but was more susceptible to necrotrophic Botrytis cinerea. Microarray performed with erf104 or mpk6 revealed only a limited number of flg22-induced genes that require these elements - possibly as a; result of functional redundancies. Thus, ERF104 phosphorylation by MPK6, in concert with ethylene signaling induced by pathogen-derived molecules, modulates defense in Arabidopsis. Experiment Overall Design: Leaves of six week old Col-0, erf104, mpk6 and 35S::ERF104 plants were infiltrated with 1ÂµM Flg22 or water and harvested four hours later. Total RNA was isolated and processed according to the Affymetrix protocol for biotin-labelled cRNA and hybridized to the Affymetrix ATH1 chip. The data Experiment Overall Design: was analyzed with Genespring GX 7.3.1 software (Agilent) with the following parameters: Filter on Flags for present or marginal in 50% of all considered experiments, Filter for reliable differentially expressed genes based on volcano plot (one way ANOVA p-value <0.05 and >3-fold change in expression). For the flg22 experiments, analysis for each genotype was separately performed and a composite list of flg22-regulated genes compiled â with the aim of including genes that may be differentially regulated in the genotype. Global expression profile was visualized by k-means clustering and condition tree (Genespring).

Dataset Information

Transcription profiling of Arabidopsis response to bacterial-(LPS, HrpZ, Flg22) and oomycete-(NPP1) derived elicitors - AtGenExpress

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets