Abstract
BackgroundUncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures.ResultsIn this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factors for the SHORT ROOT/SCARECROW and PLETHORA pathways. We further show that these networks are a powerful tool to integrate and analyze high-throughput expression data, as exemplified by our analysis of a SHORT ROOT induction time-course microarray dataset, and are a reliable source for the prediction of novel root gene functions. In particular, we used our database to predict novel genes involved in root secondary cell-wall synthesis and identified the MADS-box TF XAL1/AGL12 as an unexpected participant in this process.ConclusionsThis study demonstrates that network inference using carefully curated microarray data yields reliable TRN architectures. In contrast to previous efforts to obtain root TRNs, that have focused on particular functional modules or tissues, our root transcriptional interactions provide an overview of the transcriptional pathways present in Arabidopsis thaliana roots and will likely yield a plethora of novel hypotheses to be tested experimentally.
Highlights
Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge
We further show that the inferred TRNs can accurately predict new Transcription factor (TF) functions, as exemplified by the predicted role of the MADS-box TF XAL1/AGL12 (AT1G71692) in secondary cell wall formation and its confirmation with loss-of-function mutant root phenotypes for this gene
In order to infer the TRNs underlying root development and physiological processes in Arabidopsis, we used two carefully curated datasets obtained from 656 rootspecific CEL files from 56 ATH1 microarray experiments (Additional file 1)
Summary
Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. As of October 2010, there were 686 experiments using the ATH1 chip listed in the EBI ArrayExpress database [8] All of these experiments provide a quantitative analysis of gene expression in Arabidopsis tissues under a variety of experimental conditions and are a suitable data source for Arabidopsis Transcriptional Regulatory Network (TRN) inference. Databases such as Genevestigator [9,10], ATTED-II [11,12], or BAR Expression Angler [13,14] have tools for the analysis of Arabidopsis microarray data, they either use a limited set of microarray experiments, the AtGenExpress series [15] (ATTED-II and BAR Expression Angler), or their quality controlled, curated, annotated and normalized data is not publicly available (Genevestigator)
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