ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

• Published in: BMC plant biology Vol. 14; no. 1; p. 97
• Main Authors: Chávez Montes, Ricardo A, Coello, Gerardo, González-Aguilera, Karla L, Marsch-Martínez, Nayelli, de Folter, Stefan, Alvarez-Buylla, Elena R
• Format: Journal Article
• Language: English
• Published: London BioMed Central 16.04.2014; BioMed Central Ltd
• Subjects: Agriculture; Algorithms; Analysis; animal development; Arabidopsis - genetics; Arabidopsis - radiation effects; Arabidopsis Proteins - genetics; Arabidopsis Proteins - metabolism; Arabidopsis thaliana; Bioinformatics; Biomedical and Life Sciences; Cell Wall - metabolism; Cell Wall - radiation effects; data collection; Data Mining; Databases, Genetic; Development and cell biology; DNA binding proteins; Epistasis, Genetic - radiation effects; Gene Expression Profiling; Gene Expression Regulation, Plant - radiation effects; Gene Regulatory Networks - genetics; Genes; Genetic aspects; Genomics; High-Throughput Nucleotide Sequencing; Life Sciences; microarray technology; Mutation - genetics; Oligonucleotide Array Sequence Analysis; Physiological aspects; Plant Roots - genetics; Plant Roots - radiation effects; Plant Sciences; prediction; Research Article; rooting; roots; Time Factors; tissues; transcription (genetics); transcription factors; Transcription Factors - genetics; Transcription Factors - metabolism; Tree Biology; Ultraviolet Rays; Root; Transcription factor; Arabidopsis; Transcriptional regulatory networks; ARACNe
• ISSN: 1471-2229; 1471-2229
• DOI: 10.1186/1471-2229-14-97

Background Uncovering 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. Results In 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. Conclusions This 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.