Dataset Information

Regulatory Proteolysis in Arabidopsis-Pathogen Interactions.

ABSTRACT: Approximately two and a half percent of protein coding genes in Arabidopsis encode enzymes with known or putative proteolytic activity. Proteases possess not only common housekeeping functions by recycling nonfunctional proteins. By irreversibly cleaving other proteins, they regulate crucial developmental processes and control responses to environmental changes. Regulatory proteolysis is also indispensable in interactions between plants and their microbial pathogens. Proteolytic cleavage is simultaneously used both by plant cells, to recognize and inactivate invading pathogens, and by microbes, to overcome the immune system of the plant and successfully colonize host cells. In this review, we present available results on the group of proteases in the model plant Arabidopsis thaliana whose functions in microbial pathogenesis were confirmed. Pathogen-derived proteolytic factors are also discussed when they are involved in the cleavage of host metabolites. Considering the wealth of review papers available in the field of the ubiquitin-26S proteasome system results on the ubiquitin cascade are not presented. Arabidopsis and its pathogens are conferred with abundant sets of proteases. This review compiles a list of those that are apparently involved in an interaction between the plant and its pathogens, also presenting their molecular partners when available.

SUBMITTER: Pogany M

PROVIDER: S-EPMC4632692 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Regulatory Proteolysis in Arabidopsis-Pathogen Interactions.

Pogány Miklós M Dankó Tamás T Kámán-Tóth Evelin E Schwarczinger Ildikó I Bozsó Zoltán Z

International journal of molecular sciences 20150924 10

Approximately two and a half percent of protein coding genes in Arabidopsis encode enzymes with known or putative proteolytic activity. Proteases possess not only common housekeeping functions by recycling nonfunctional proteins. By irreversibly cleaving other proteins, they regulate crucial developmental processes and control responses to environmental changes. Regulatory proteolysis is also indispensable in interactions between plants and their microbial pathogens. Proteolytic cleavage is simu ...[more]

PMID: 26404238

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Project description:Prediction of transcriptional regulatory mechanisms in Arabidopsis has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work 1, that a combination of correlation measurements and cis-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF-->target relationships on a genome-wide scale.We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and cis-regulatory element (CRE) detection (>or=1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor (TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF-->target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the >or=1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions.These findings provide an approach and guidance for researchers interested in predicting transcriptional regulatory mechanisms using microarray data that they generate (or microarray data that is publically available) combined with CRE detection in promoter sequence data.

Project description:The goal of this project is to understand the etiology and pathology of host-pathogen interaction using high-throughput cellular phenotyping. By combining the expertise in pathogen biology, informatics and stem cell biology across the institute, this project will use targeted genetic modification in mouse embryonic stem cells to identify functions of host genes in innate immunity. We will also explore the biology of mouse embryonic stem cells and their differentiation into immune competent cells. To this end we will generate homozygous and inducible knockout lines in mouse embryonic stem cells, which will be differentiated into immune competent cells, such as macrophages or dendritic cells. These will be subjected to a panel of immune challenge and stimulation assays to characterise the functions of the disrupted genes. To identify new functions for mammalian genes in innate immunity we will:\Generate a panel of homozygous inducible gene knockout mouse ES cell lines working from a list of ca. 80 candidate genes (MGP phenotyping and GWAS hits). \Scale up differentiation protocols for immune challenge and stimulation assays.\Benchmark in vitro differentiated ES cell models against primary immune cells from selected MGP mouse lines and eventually against human iPS cells.\Screen a panel of 30 genetically modified cell lines in immunological challenge and stimulation assays, defining phenotypes at the cellular level by high content imaging, biochemically by assaying cytokine production, and at the molecular level by transcriptome analysis.\Define novel gene functions in innate immunity through network analysis of gene expression data from all challenged cell lines.\In the longer term scale up the production and phenotyping of genetically modified cell lines (mouse or human as appropriate).This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Dataset Information

Regulatory Proteolysis in Arabidopsis-Pathogen Interactions.

Publications

Regulatory Proteolysis in Arabidopsis-Pathogen Interactions.

Similar Datasets

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