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Background  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.    Results  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.    Conclusions  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.

Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate