Research

Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions

Javier Carrera^1,2* , Guillermo Rodrigo^1* , Alfonso Jaramillo^3,4 and Santiago F Elena^1,5

¹  Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas-UPV, Ingeniero Fausto Elio s/n, 46022 València, Spain

²  ITACA, Universidad Politécnica de Valencia, Ingeniero Fausto Elio s/n, 46022 València, Spain

³  Laboratoire de Biochimie, École-Polytechnique-CNRS UMR7654, Route de Saclay, 91128 Palaiseau, France

⁴  Epigenomics Project, Genopole-Université d'Évry Val d'Essonne-CNRS UPS3201, 523 Terrasses de l'Agora, 91034 Évry, France

⁵  The Santa Fe Institute, Hyde Park Road, Santa Fe, NM 87501, USA

author email corresponding author email* Contributed equally

Genome Biology 2009, 10:R96doi:10.1186/gb-2009-10-9-r96

Published:	15 September 2009

Subject areas: Bioinformatics, Molecular biology, Plant biology

Abstract

Background

Understanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth conditions, developmental stages, biotic and abiotic stresses, and a variety of mutant genotypes.

Results

We show that the A. thaliana regulatory network has the characteristic properties of hierarchical networks. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments not included in the training dataset. We also used our model to analyze the robustness in expression levels conferred by network motifs such as the coherent feed-forward loop. In addition, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures.

Conclusions

These data suggest that A. thaliana has evolved high connectivity in terms of transcriptional regulation among cellular functions involved in response and adaptation to changing environments, while gene networks constitutively expressed or less related to stress response are characterized by a lower connectivity. Taken together, these findings suggest conserved regulatory strategies that have been selected during the evolutionary history of this eukaryote.