The long-term goal of this project is to dynamically model the regulatory networks controlling nitrogen (N) uptake/assimilation in plants. The systems biology approaches that integrate genomic data have generated testable hypotheses for regulatory motifs controlling N-uptake/assimilation in response to nitrogen sensing. The overriding hypothesis being tested is that inorganic-N signals (nitrate) activate motifs involved in regulating nitrate uptake, reduction and assimilation into 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. As the key genes for N-assimilation utilize ATP and NADH, we posit that the associated regulatory motif components constitute an "energy" conservation mechanism, activating N-assimilation when inorganic-N is available, and repressing/storing it when organic-N levels are high. Using mutants, transgenics, and chromatin-IP, roles for transcription factor (TF) hubs, TF-TF motifs, and miRNA-TFs motifs regulating genes in N-assimilation were validated. This renewal proposes to use these validated regulatory components, denoted sentinels, to fuel a new round of genome-scale testing and gene-specific experimentation to seed the growth of the network and to create a time-dependent dynamic visual presentation model that will detail the flow of N-signal propagation through the N-assimilatory regulatory network in four aims: 1. Test hypotheses for the function of validated TFs and TF-motifs in regulating N-uptake/assimilation in response to inorganic-N or organic-N sensing. 2. Use validated TFs as sentinels to fuel genome-wide discovery of interacting partners. Generate time-course transcriptome data and use an inducible transgenic system to identify both direct and indirect targets system-wide. 3. Test hypotheses for post-transcriptional and post-translational mechanisms predicted by the current network models and generate metabolomic data for incorporation into these models. 4. Analyze and visualize the genomic datasets from time-course and transgenic studies, to generate a time- varying (dynamic) combinatorial view of the core regulatory mechanisms, whether transcriptional, post- transcriptional or post-translational, and their effect on propagating the N-signal through the N-assimilation regulatory network. The growth of this first validated metabolic regulatory network in plants will uncover: i) the topology of regulatory networks in plants including the role of network motifs for comparison to other organisms, ii) mechanisms that control N-use efficiency. 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. The systems approach, identification of sentinel genes, related neighbors, conditional expression analysis, and circuit formation can be applied to any species with available genome data and will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with applications to medicine.

Public Health Relevance

Our long-term goal is to combine systems biology, genomic and genetic approaches to model the regulatory networks controlling nitrogen-uptake/assimilation in response to nitrogen signals and interactions. Our proposal aims to allow us to model, predict and test how perturbations of these regulatory networks may be used to enhance N-use efficiency in plants, which will have a significant impact on energy-use, reduce nitrate contamination of the environment and improve human nutrition. Moreover, as the systems biology approaches and tools we have and will continue to develop can be applied to any species for which genome data is available, these studies will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with applications to medicine.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM032877-28
Application #
8258262
Study Section
Molecular Genetics B Study Section (MGB)
Program Officer
Reddy, Michael K
Project Start
1983-12-01
Project End
2014-04-30
Budget Start
2012-05-01
Budget End
2014-04-30
Support Year
28
Fiscal Year
2012
Total Cost
$366,789
Indirect Cost
$124,214
Name
New York University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
041968306
City
New York
State
NY
Country
United States
Zip Code
10012
Renfrew, P Douglas; Craven, Timothy W; Butterfoss, Glenn L et al. (2014) A rotamer library to enable modeling and design of peptoid foldamers. J Am Chem Soc 136:8772-82
Levine, Paul M; Craven, Timothy W; Bonneau, Richard et al. (2014) Intrinsic bioconjugation for site-specific protein PEGylation at N-terminal serine. Chem Commun (Camb) 50:6909-12
Levine, Paul M; Craven, Timothy W; Bonneau, Richard et al. (2014) Semisynthesis of peptoid-protein hybrids by chemical ligation at serine. Org Lett 16:512-5
Para, Alessia; Li, Ying; Marshall-Colón, Amy et al. (2014) Hit-and-run transcriptional control by bZIP1 mediates rapid nutrient signaling in Arabidopsis. Proc Natl Acad Sci U S A 111:10371-6
Youngs, Noah; Penfold-Brown, Duncan; Bonneau, Richard et al. (2014) Negative example selection for protein function prediction: the NoGO database. PLoS Comput Biol 10:e1003644
Laganà, Alessandro; Acunzo, Mario; Romano, Giulia et al. (2014) miR-Synth: a computational resource for the design of multi-site multi-target synthetic miRNAs. Nucleic Acids Res 42:5416-25
Tchourine, Konstantine; Poultney, Christopher S; Wang, Li et al. (2014) One third of dynamic protein expression profiles can be predicted by a simple rate equation. Mol Biosyst 10:2850-62
Youngs, Noah; Penfold-Brown, Duncan; Drew, Kevin et al. (2013) Parametric Bayesian priors and better choice of negative examples improve protein function prediction. Bioinformatics 29:1190-8
Vidal, Elena A; Moyano, Tomas C; Krouk, Gabriel et al. (2013) Integrated RNA-seq and sRNA-seq analysis identifies novel nitrate-responsive genes in Arabidopsis thaliana roots. BMC Genomics 14:701
Gifford, Miriam L; Banta, Joshua A; Katari, Manpreet S et al. (2013) Plasticity regulators modulate specific root traits in discrete nitrogen environments. PLoS Genet 9:e1003760

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