Arabidopsis N-regulatory network dynamics: Integrating metabolome & transcriptome

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The primary goal of the proposed research is to use a systems biology approach to collectively analyze and integrate time-dependent data from the transcriptome, metabolome, and fluxome components of the N- regulatory network controlling N-assimilation in Arabidopsis. This integrative approach will allow us to dynamically model the flow of N-signal propagation through the N-regulatory network on a systems-wide level and identify the transcriptional cascade involved in this regulation. This goal will be achieved through four aims: 1. Creation of high-resolution dynamic transcriptome datasets to generate a time-dependent nitrogen regulatory network, by performing microarray analysis on Arabidopsis roots and shoots treated with nitrate over a time course. 2. Quantification of metabolite levels and metabolic flux in the N-assimilatory network in response to N-signal, using stable isotope labeled N15 over a time course. 3. Integration of transcriptome, metabolome, and fluxome data to create a time-dependent dynamic network model for the control of N- uptake/assimilation, using a series of analytical techniques including lag correlation, linear regression, and machine learning (state space analysis). 4. Functional validation of regulatory network predictions by testing model generated hypotheses with T-DNA mutants and inducible expression systems. The overriding hypothesis being tested is that inorganic-N signals (nitrate) activate motifs involved in regulating nitrate uptake, reduction and assimilation into organic-N (Glu/Gln), used for biosynthetic reactions. The organic-N products (Glu/Gln) in turn activate motifs controlling Asn synthesized for N-storage, and repress ones controlling N- uptake/assimilation. The proposed research will allow me to identify the regulatory genes responding to these inorganic and organic N-signals that regulate genes in the N-uptake and assimilation pathways by integrating genome wide transcriptomic data with metabolomic data. The synthesis of these aims should allow for modeling, predicting and testing how perturbations of the "system" may be used to enhance N-use efficiency, which impacts energy-use (fertilizers/biofuels), nitrate contamination of the environment and human nutrition.