GEM2Net: from gene expression modeling to -omics networks, a new CATdb module to investigate Arabidopsis thaliana genes involved in stress response

Abstract

CATdb (http://urgv.evry.inra.fr/CATdb) is a database providing a public access to a large collection of transcriptomic data, mainly for Arabidopsis but also for other plants. This resource has the rare advantage to contain several thousands of microarray experiments obtained with the same technical protocol and analyzed by the same statistical pipelines. In this paper, we present GEM2Net, a new module of CATdb that takes advantage of this homogeneous dataset to mine co-expression units and decipher Arabidopsis gene functions. GEM2Net explores 387 stress conditions organized into 18 biotic and abiotic stress categories. For each one, a model-based clustering is applied on expression differences to identify clusters of co-expressed genes. To characterize functions associated with these clusters, various resources are analyzed and integrated: Gene Ontology, subcellular localization of proteins, Hormone Families, Transcription Factor Families and a refined stress-related gene list associated to publications. Exploiting protein–protein interactions and transcription factors-targets interactions enables to display gene networks. GEM2Net presents the analysis of the 18 stress categories, in which 17 264 genes are involved and organized within 681 co-expression clusters. The meta-data analyses were stored and organized to compose a dynamic Web resource.

Venus flytrap carnivorous lifestyle builds on herbivore defense strategies

“…This results in the production of substances toxic to herbivores or in making the plant meal indigestible. To test the hypothesis that the overall transcriptomic response of insect-stimulated Dionaea traps resembles defined stress-related responses known from noncarnivorous plants, we performed an interspecies comparison of active Dionaea plants to known coexpressed gene clusters in A. thaliana (Zaag et al 2015). Taking advantage of the Complete Arabidopsis Transcriptome MicroArray (CATMA) gene expression reference database for Arabidopsis, we identified a diverse pattern of validated abiotic and biotic stress-related gene clusters (Supplemental Table S17) up-regulated in active traps.…”

Meta‐analysis and meta‐regression of transcriptomic responses to water stress in Arabidopsis

“…Meta-analysis has been used in other fields to synthesize transcriptomic data, and should also be more widely implemented for plant differential response data. Shaik and Ramakrishna (2013) and Zaag et al (2015) synthesized DEG microarray studies on biotic and abiotic stress responses in Arabidopsis, and identified genes that were differentially expressed in response to each stress. Although these approaches were each shown to be effective in various ways, for example in assisting with functional annotations and visualizing co-expression relationships, the disadvantages of these approaches are that they do not weight studies based on precision (i.e.…”

“…A study by Zaag et al . () used a model‐based clustering approach to find and visualize co‐expressed genes involved in the response of Arabidopsis to stress; this approach was applied to each of 18 stress categories. However, these approaches are limited because they do not account for differential variance among experiments, nor do they model methodological and biological covariates within or across stress categories.…”

“…While there are few such studies in plants, one that we are aware of combined differential gene expression results to drought and bacterial stress in Arabidopsis and rice using both a rank-based non-parametric as well as a co-expression approach (Shaik and Ramakrishna, 2013). A study by Zaag et al (2015) used a model-based clustering approach to find and visualize coexpressed genes involved in the response of Arabidopsis to stress; this approach was applied to each of 18 stress categories. However, these approaches are limited because they do not account for differential variance among experiments, nor do they model methodological and biological covariates within or across stress categories.…”

DiCoExpress: a tool to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models.

“…Gaussian mixture models were relevant for microarray data and were applied with success on several datasets [35,36]. For RNAseq data, which are discrete, Rau et al [16] first concluded that normalized expression profiles modelled with a Poisson mixture are relevant for co-expression analysis.…”