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.
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.
|Para, Alessia; Li, Ying; Coruzzi, Gloria M (2018) ?ChIP-Seq for Genome-Wide Mapping of In Vivo TF-DNA Interactions in Arabidopsis Root Protoplasts. Methods Mol Biol 1761:249-261|
|Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard (2018) deepNF: deep network fusion for protein function prediction. Bioinformatics 34:3873-3881|
|Tipton, Laura; Müller, Christian L; Kurtz, Zachary D et al. (2018) Fungi stabilize connectivity in the lung and skin microbial ecosystems. Microbiome 6:12|
|Varala, Kranthi; Marshall-Colón, Amy; Cirrone, Jacopo et al. (2018) Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants. Proc Natl Acad Sci U S A 115:6494-6499|
|Swift, Joseph; Coruzzi, Gloria M (2017) A matter of time - How transient transcription factor interactions create dynamic gene regulatory networks. Biochim Biophys Acta Gene Regul Mech 1860:75-83|
|Baugh, Evan H; Simmons-Edler, Riley; Müller, Christian L et al. (2016) Robust classification of protein variation using structural modelling and large-scale data integration. Nucleic Acids Res 44:2501-13|
|Ristova, Daniela; Carré, Clément; Pervent, Marjorie et al. (2016) Combinatorial interaction network of transcriptomic and phenotypic responses to nitrogen and hormones in the Arabidopsis thaliana root. Sci Signal 9:rs13|
|Raviram, Ramya; Rocha, Pedro P; Müller, Christian L et al. (2016) 4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments. PLoS Comput Biol 12:e1004780|
|Doidy, Joan; Li, Ying; Neymotin, Benjamin et al. (2016) ""Hit-and-Run"" transcription: de novo transcription initiated by a transient bZIP1 ""hit"" persists after the ""run"". BMC Genomics 17:92|
|Varala, Kranthi; Li, Ying; Marshall-Colón, Amy et al. (2015) ""Hit-and-Run"" leaves its mark: catalyst transcription factors and chromatin modification. Bioessays 37:851-6|
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