Luca Mizzi

Transcriptomics studies have been facilitated by the development of microarray and RNA-Seq technologies, with thousands of expression datasets available for many species. However, the quality of data can be highly variable, making the combined analysis of different datasets difficult and unreliable. Most of the microarray data for Medicago truncatula, the barrel medic, have been stored and made publicly accessible on the web database Medicago truncatula Gene Expression atlas (MtGEA). The aim of this work is to ameliorate the quality of the MtGEA database through a general method based on logical and statistical relationships among parameters and conditions. The initial 716 columns available in the dataset were reduced to 607 by evaluating the quality of data through the sum of the expression levels over the entire transcriptome probes and Pearson correlation among hybridizations. The reduced dataset shows great improvements in the consistency of the data, with a reduction in both false positives and false negatives resulting from Pearson correlation and GO enrichment analysis among genes. The approach we used is of general validity and our intent is to extend the analysis to other plant microarray databases.

Background: The WRKY transcription factor gene family has a very ancient origin and has undergone extensive duplications in the plant kingdom. Several studies have pointed out their involvement in a range of biological processes, revealing that a large number ofWRKYgenes are transcriptionally regulated under conditions of biotic and/or abiotic stress. To investigate the existence ofWRKYco-regulatory networks in plants, a whole gene familyWRKYs expression study was carried out in rice (Oryza sativa). This analysis was extended toArabidopsis thalianataking advantage of an extensive repository of gene expression data.Results: The presented results suggested that 24 members of the riceWRKYgene family (22% of the total) were differentially-regulated in response to at least one of the stress conditions tested. We defined the existence of nine OsWRKYgene clusters comprising both phylogenetically related and unrelated genes that were significantly co-expressed, suggesting that specific sets ofWRKYgenes might act in co-regulatory networks. This hypothesis was tested by Pearson Correlation Coefficient analysis of the ArabidopsisWRKYgene family in a large set of Affymetrix microarray experiments.AtWRKYswere found to belong to two main co-regulatory networks (COR-A, COR-B) and two smaller ones (COR-C and COR-D), all including genes belonging to distinct phylogenetic groups. The COR-A network contained severalAtWRKYgenes known to be involved mostly in response to pathogens, whose physical and/or genetic interaction was experimentally proven. We also showed that specific co-regulatory networks were conserved between the two model species by identifying Arabidopsis orthologs of the co-expressedOsWRKYgenes.Conclusion: In this work we identified sets of co-expressedWRKYgenes in both rice and Arabidopsis that are functionally likely to cooperate in the same signal transduction pathways. We propose that, making use of data from co-regulatory networks, it is possible to highlight novel clusters of plant genes contributing to the same biological processes or signal transduction pathways. Our approach will contribute to unveil gene cooperation pathways not yet identified by classical genetic analyses. This information will open new routes contributing to the dissection of WRKY signal transduction pathways in plants.