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A genomic approach to identify regulatory nodes in the transcriptional network of systemic acquired resistance in plants.

ABSTRACT: Many biological processes are controlled by intricate networks of transcriptional regulators. With the development of microarray technology, transcriptional changes can be examined at the whole-genome level. However, such analysis often lacks information on the hierarchical relationship between components of a given system. Systemic acquired resistance (SAR) is an inducible plant defense response involving a cascade of transcriptional events induced by salicylic acid through the transcription cofactor NPR1. To identify additional regulatory nodes in the SAR network, we performed microarray analysis on Arabidopsis plants expressing the NPR1-GR (glucocorticoid receptor) fusion protein. Since nuclear translocation of NPR1-GR requires dexamethasone, we were able to control NPR1-dependent transcription and identify direct transcriptional targets of NPR1. We show that NPR1 directly upregulates the expression of eight WRKY transcription factor genes. This large family of 74 transcription factors has been implicated in various defense responses, but no specific WRKY factor has been placed in the SAR network. Identification of NPR1-regulated WRKY factors allowed us to perform in-depth genetic analysis on a small number of WRKY factors and test well-defined phenotypes of single and double mutants associated with NPR1. Among these WRKY factors we found both positive and negative regulators of SAR. This genomics-directed approach unambiguously positioned five WRKY factors in the complex transcriptional regulatory network of SAR. Our work not only discovered new transcription regulatory components in the signaling network of SAR but also demonstrated that functional studies of large gene families have to take into consideration sequence similarity as well as the expression patterns of the candidates.

SUBMITTER: Wang D

PROVIDER: S-EPMC1635530 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Genome-wide microarray analyses revealed that during biological activation of systemic acquired resistance (SAR) in Arabidopsis, the transcript levels of several hundred plant genes were consistently up- (SAR(+) genes) or down-regulated (SAR(-) genes) in systemic, non-inoculated leaf tissue. This transcriptional reprogramming fully depended on the SAR regulator FLAVIN-DEPENDENT MONOOXYGENASE1 (FMO1). Functional gene categorization showed that genes associated with salicylic acid (SA)-associated defenses, signal transduction, transport, and the secretory machinery are overrepresented in the group of SAR(+) genes, and that the group of SAR(-) genes is enriched in genes activated via the jasmonate (JA)/ethylene (ET)-defense pathway, as well as in genes associated with cell wall remodeling and biosynthesis of constitutively produced secondary metabolites. This suggests that SAR-induced plants reallocate part of their physiological activity from vegetative growth towards SA-related defense activation. Alignment of the SAR expression data with other microarray information allowed us to define three clusters of SAR(+) genes. Cluster I consists of genes tightly regulated by SA. Cluster II genes can be expressed independently of SA, and this group is moderately enriched in H2O2- and abscisic acid (ABA)-responsive genes. The expression of the cluster III SAR(+) genes is partly SA-dependent. We propose that SA-independent signaling events in early stages of SAR activation enable the biosynthesis of SA and thus initiate SA-dependent SAR signaling. Both SA-independent and SA-dependent events tightly co-operate to realize SAR. SAR(+) genes function in the establishment of diverse resistance layers, in the direct execution of resistance against different (hemi-)biotrophic pathogen types, in suppression of the JA- and ABA-signaling pathways, in redox homeostasis, and in the containment of defense response activation. Our data further indicated that SAR-associated defense priming can be realized by partial pre-activation of particular defense pathways.

Project description:BackgroundAlzheimer's disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.ResultsOur approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.ConclusionsWith the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Project description:BackgroundGastric carcinoma is a very diverse disease. The progression of gastric carcinoma is influenced by complicated gene networks. This study aims to investigate the actual and potential prognostic biomarkers related to survival in gastric carcinoma patients to further our understanding of tumor biology.MethodsA weighted gene co-expression network analysis was performed with a transcriptome dataset to identify networks and hub genes relevant to gastric carcinoma prognosis. Data was obtained from 300 primary gastric carcinomas (GSE62254). A validation dataset (GSE34942 and GSE15459) and TCGA dataset confirmed the results. Gene ontology, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA) were performed to identify the clusters responsible for the biological processes and pathways of this disease.ResultsA brown transcriptional module enriched in the organizational process of the extracellular matrix was significantly correlated with overall survival (HR = 1.586, p = 0.005, 95% CI [1.149-2.189]) and disease-free survival (HR = 1.544, p = 0.008, 95% CI [1.119-2.131]). These observations were confirmed in the validation dataset (HR = 1.664, p = 0.006, 95% CI [1.155-2.398] in overall survival). Ten hub genes were identified and confirmed in the validation dataset from this brown module; five key biomarkers (COL8A1, FRMD6, TIMP2, CNRIP1 and GPR124 (ADGRA2)) were identified for further research in microsatellite instability (MSI) and epithelial-tomesenchymal transition (MSS/EMT) gastric carcinoma molecular subtypes. A high expression of these genes indicated a poor prognosis.ConclusionA transcriptional co-expression network-based approach was used to identify prognostic biomarkers in gastric carcinoma. This method may have potential for use in personalized therapies, however, large-scale randomized controlled clinical trials and replication experiments are needed before these key biomarkers can be applied clinically.

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A genomic approach to identify regulatory nodes in the transcriptional network of systemic acquired resistance in plants.

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