Characterisation of BRH1, a brassinosteroid-responsive RING-H2 gene from Arabidopsis thalianaAn emerging brassinosteroid-respohsive in brassinosteroid-responsive research focuses brassinosteroid-responsive antagonism between regulatory brassinosteroid-responsive governing growth brasdinosteroid-responsive immunity. Such crosstalk represents brassinosteroid-responsive point of vulnerability for pathogens to exploit. Transgenic potato Solanum tuberosum plants hgh melhores marcas the effector exhibit transcriptional and phenotypic hallmarks of over-active BR signalling, and show enhanced susceptibility to P. Microarray analysis was used to identify a set of Brassinosteroid-responsive marker genes in potato, all of which are constitutively expressed to BR-induced levels in AVR2 transgenic lines. Thus Brassinosteroid-responsive exploits crosstalk between BR signalling brassinosteroid-responsive innate immunity in Solanum species, representing a novel, indirect mode of innate immune suppression by a filamentous pathogen effector. We only request your email address so that the person you are recommending the page to knows that brassinosterlid-responsive wanted them to see it, brassinosteroid-responsive that it is not junk mail. We do not capture any email address.
The comprehensive systems-biology database CSB. DB was used to reveal brassinosteroid BR -related genes from expression profiles based on co-response analyses. Genes exhibiting simultaneous changes in transcript levels are candidates of common transcriptional regulation.
Combining numerous different experiments in data matrices allows ruling out outliers and conditional changes of transcript levels. As expected, these genes comprised pathway-involved genes e. But transcript co-response takes the analysis a step further compared with direct approaches because BR-related non BR-responsive genes were identified. Insights into networks and the functional context of genes are provided, because factors determining expression patterns are reflected in correlations.
Our findings demonstrate that transcript co-response analysis presents a valuable resource to uncover common regulatory patterns of genes. Different data matrices in CSB. DB allow examination of specific biological questions. All matrices are publicly available through CSB. This work presents one possible roadmap to use the CSB. Brassinosteroids BRs are highly potent growth-promoting sterol derivatives.
BR-deficient and BR-insensitive mutants in Arabidopsis , pea, tomato, barley and rice show dwarfism 1. Other BR-regulated genes point to further mechanisms contributing to growth. BR apparently coordinates diverse processes, partly through interactions with other phytohormones.
Enhanced resistance of BR-treated plants to temperature, salt, water, phytopathogens and other environmental stresses has been reported 2 — 4. However, underlying molecular mechanisms are unknown. The growth effects of exogenous BR are light-dependent.
Arabidopsis mutants such as det2 , cpd and bri1 display short hypocotyls, opened cotyledons, and emergence of primary leaves in darkness. These findings suggest a cross-talk between photomorphogenesis and steroid signal transduction 5.
In several studies, BR-responsive gene expression in Arabidopsis was analysed 6 — Comparisons of expression profiling experiments revealed that the majority of identified genes do not show consistent BR-dependent expression in different genotypes, environmental conditions, developmental stages and tissues, and upon BR-treatment 11 , Thus, gene expression patterns are conditional, and the identified genes probably present only a subset of genes involved in BR-responses.
Another reason for the incomplete discovery of genomic effects is the hitherto limited number of experiments, as gene expression varies even under highly controlled conditions. Many genes fail to meet the stringent selection criteria routinely applied in expression profiling experiments.
Cross-experiment co-response analysis provides an alternative approach which is based on the assumption that common transcriptional control of genes should be reflected in synchronous changes in transcript levels. Co-response analysis describes common changes of transcript levels among gene pairs. Publicly available expression profiles represent a rich resource for cross-experiment investigations.
We demonstrate means of in silico cross-checking and confirmation using the publicly available Affymetrix expression profiles provided by the AtGenExpress consortium.
In addition, 44 cell wall and growth-related genes were selected for wet-lab experimental validation and subsequent real-time RT—PCR. Transcript co-responses were retrieved from CSB. DB [a comprehensive systems-biology database; http: A total of expression profiles from 22 experiments were obtained from NASCarrays [ http: XLS sheet 11 nasc profiles ].
In most cases, 2 or 3 profiles per experiment with the highest numbers of Present and Marginal calls were selected for nasc The nasc and nasc matrices were generated from profiles which ranked 2nd and 3rd according to the numbers of Present and Marginal calls.
The nasc matrix was based on 51 profiles and represents genes. The nasc matrix was based on 49 experiments and represents genes. Correlations were based on non-parametric Spearman's rank-order correlation r s. From the initial result with the nasc matrix which covers associated genes , genes were selected and used for statistical in-depth analyses. To test for influence of individual or small subsets of underlying expression profiles, transcriptional co-responses were confirmed by bootstrap analyses.
Bootstrap spearman correlation, probability, confidence interval and power of test were re-computed using the statistical software environment R http: Assignments of genes into bins were taken from the MapMan software Obtained assignments were slightly modified for bins that cover few genes. Merging of bins is illustrated in Table SI Supplementary information.
The distribution of retrieved genes into functional categories was computed by adding-up the relative assignment coefficient for each gene per category. Two growth conditions were applied. Plants were harvested 14 or 19 days after sowing. Alternatively, Arabidopsis thaliana cv.
Aerial organs were harvested 28 days after sowing. Normalization and expression analysis were performed with the MAS 5. Output of all experiments was multiplied by a scaling factor to adjust its average intensity to a target intensity of Results of Absolute and Comparison expression analysis were imported into MS Access and screened for significant changes.
The Detection algorithm calculates detection p -values and assigns Present, Marginal or Absent calls. Standard parameters were applied to remove genes with Absent and Marginal calls. Simultaneously, Affymetrix Change and Signal Log Ratio algorithms were used in order to identify changes with high reliability. The Change algorithm is based on Wilcoxon's signed rank test and produces a final change p -value.
This p -value ranges from 0. The signal log ratio estimates the magnitude and direction of change of a transcript. Central part of CSB. DB is a set of co-response databases CoR. DBs which are based on publicly available transcript profiles.
The basic assumption is that common transcriptional control of genes is accompanied by synchronous changes of transcript levels. Scanning for best co-responses among changing transcript levels allows the deduction of hypotheses about common regulation of genes We propose a general strategy to exploit CSB.
The strategy is illustrated in Figure 1. The biological question determines the choice of the type of data matrix. After the selection of a suitable data matrix, useful guide genes have to be identified. The expression pattern and function determine the suitability of guide genes. Therefore, biological knowledge is required. The guide genes are used to screen for genes which show similar expression patterns in the profiles underlying a data matrix. Statistical methods and experiments allow testing of the identified genes.
In the following section, the strategy is demonstrated using BR-related genes. Complex data matrices can be assembled by selecting transcript profiles representing many different experimental conditions. Complex data matrices include different genotypes, environmental conditions and developmental stages. To identify BR-related genes, the complex nasc database was used. The matrix comprised 51 expression profiles [ http: XLS sheet 11 nasc profiles ] representing a wide range of experimental conditions with a minimal overlap of identical experiments.
All expression profiles were normalized using the MAS 5. Transcript measurements were required to have high quality, i. Thus, the nasc matrix contained only accessible genes. Two other complex matrices nasc and nasc were established using additional expression profiles [supplement.
These matrices were used to test and confirm the results obtained with the nasc matrix. The nasc and nasc matrices consisted of 51 and 49 expression profiles and represented and genes, respectively. Biological knowledge is required for the selection of useful guide genes. The screen for BR-related genes shows that different options emerge. Three different classes of genes could be used: However, Arabidopsis BRI1 may bind other ligands.
Therefore, BAK1 was included as a second guide gene. Thus, the requirements for subsequent intersection analysis were met. Transcript co-response calculations are based on changes in mRNA levels within the underlying expression profiles. Spearman's non-parametric rank correlation coefficient r s , p -value and power were used to retrieve transcript co-responses. Use of known BR-responsive genes for co-response analyses reveals further BR-responsive genes Table 3 ; statistical parameters below.
However, BR-responsive genes are also likely to be regulated by other factors, and the functional context is unclear. However, transcripts of other BR-biosynthesis genes were excluded from the data matrices because of quality concerns and thus could not be tested. Use of BR-signalling components presents a third alternative. BRI1 is an essential receptor component for BR-responses. BAK1 was identified independently by a yeast two-hybrid screen for BRI1-interacting proteins and as suppressor of a weak bri1 allele.