1. Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants
- Author
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W. Richard McCombie, Sophie Léran, Molly B. Edwards, Tara M. Rock, Angelo Pasquino, Matthew D. Brooks, Shipra Mittal, Kranthi Varala, Grace Kim, Sandrine Ruffel, Dennis Shasha, Amy Marshall-Colon, Gloria M. Coruzzi, Jacopo Cirrone, Center for Plant Biology, Horticulture and Landscape Architecture, Purdue University [West Lafayette], Department of Plant Biology, University of Illinois at Urbana-Champaign [Urbana], University of Illinois System, Courant institute for Mathematical Sciences, New York University [New York] (NYU), NYU System (NYU), Department of Biology, University of Minho, Biochimie et Physiologie Moléculaire des Plantes (BPMP), Université de Montpellier (UM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Equipe Hormones, Nutriments et Développement (HoNuDe) (HONUDE), Université de Montpellier (UM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Cold Spring Harbor Laboratory, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Cold Spring Harbor Laboratory (CSHL)
- Subjects
0301 basic medicine ,réseau de régulation de géne ,Transcription, Genetic ,[SDV]Life Sciences [q-bio] ,Arabidopsis ,Gene regulatory network ,F30 - Génétique et amélioration des plantes ,Transcriptome ,Assimilation des nitrates ,Gene Expression Regulation, Plant ,Transcription (biology) ,Gene Regulatory Networks ,biologie de la plante ,transcriptional dynamics ,plant biology ,2. Zero hunger ,Multidisciplinary ,food and beverages ,systems biology ,Biological Sciences ,humanities ,Nutrition des plantes ,3. Good health ,Algorithms ,Protein Binding ,Signal Transduction ,biologie des systèmes ,Logic ,Nitrogen ,Azote ,Systems biology ,Analyse de réseau ,Computational biology ,Biology ,03 medical and health sciences ,stomatognathic system ,Commentaries ,Transcription factor ,Gene ,gene regulatory networks [EN] ,assimilation azotée ,Models, Genetic ,Transcription génique ,Arabidopsis Proteins ,Gene Expression Profiling ,fungi ,nitrogen assimilation ,Intelligence artificielle ,biology.organism_classification ,F60 - Physiologie et biochimie végétales ,Gene expression profiling ,network inference ,030104 developmental biology ,facteur de transcription ,Software ,Transcription Factors - Abstract
Significance Our study exploits time—the relatively unexplored fourth dimension of gene regulatory networks (GRNs)—to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. We introduce several conceptual innovations to the analysis of time-series data in the area of predictive GRNs. Our resulting network now provides the “transcriptional logic” for transcription factor perturbations aimed at improving N-use efficiency, an important issue for global food production in marginal soils and for sustainable agriculture. More broadly, the combination of the time-based approaches we develop and deploy can be applied to uncover the temporal “transcriptional logic” for any response system in biology, agriculture, or medicine., This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our “just-in-time” analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to “prune” the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF “N-specificity” index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs—CRF4, SNZ, CDF1, HHO5/6, and PHL1—validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and 15NO3− uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal “transcriptional logic” for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.
- Published
- 2018
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