Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate
1 Center for Genomics and Systems Biology, Department of Biology, New York University, 100 Washington Square East, 1009 Main Building, New York, NY 10003, USA
2 Biochimie et Physiologie Moléculaire des Plantes, UMR 5004 CNRS/INRA/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, Place Viala, 34060 Montpellier Cedex, France
3 Courant Institute of Mathematical Sciences, New York University, New York, NY 10003, USA
Genome Biology 2010, 11:R123 doi:10.1186/gb-2010-11-12-r123Published: 23 December 2010
Nitrate, acting as both a nitrogen source and a signaling molecule, controls many aspects of plant development. However, gene networks involved in plant adaptation to fluctuating nitrate environments have not yet been identified.
Here we use time-series transcriptome data to decipher gene relationships and consequently to build core regulatory networks involved in Arabidopsis root adaptation to nitrate provision. The experimental approach has been to monitor genome-wide responses to nitrate at 3, 6, 9, 12, 15 and 20 minutes using Affymetrix ATH1 gene chips. This high-resolution time course analysis demonstrated that the previously known primary nitrate response is actually preceded by a very fast gene expression modulation, involving genes and functions needed to prepare plants to use or reduce nitrate. A state-space model inferred from this microarray time-series data successfully predicts gene behavior in unlearnt conditions.
The experiments and methods allow us to propose a temporal working model for nitrate-driven gene networks. This network model is tested both in silico and experimentally. For example, the over-expression of a predicted gene hub encoding a transcription factor induced early in the cascade indeed leads to the modification of the kinetic nitrate response of sentinel genes such as NIR, NIA2, and NRT1.1, and several other transcription factors. The potential nitrate/hormone connections implicated by this time-series data are also evaluated.