Combinatorial interaction network of transcriptomic and phenotypic responses to nitrogen and hormones in the Arabidopsis thaliana root
2016
Ristova, Daniela | Carre, Clement | Pervent, Marjorie | Medici, Anna | Kim, Grace Jaeyoon | Scalia, Domenica | Ruffel, Sandrine | Birnbaum, Kenneth D | Lacombe, Benoît | Busch, Wolfgang | Coruzzi, Gloria M. | Krouk, Gabriel | Center for Genomics and Systems Biology | New York University [New York] (NYU) ; NYU System (NYU) | Gregor Mendel Institute | Austrian Academy of Sciences (OeAW) | Biochimie et Physiologie Moléculaire des Plantes (BPMP) ; Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | Centre National de la Recherche Scientifique (CNRS)
Plants form the basis of the food webs that sustain animal life. Exogenous factors, such as nutrients and sunlight, and endogenous factors, such as hormones, cooperate to control both the growth and the development of plants. We assessed how Arabidopsis thaliana integrated nutrient and hormone signaling pathways to control root growth and development by investigating the effects of combinatorial treatment with the nutrients nitrate and ammonium; the hormones auxin, cytokinin, and abscisic acid; and all binary combinations of these factors. We monitored and integrated short-term genome-wide changes in gene expression over hours and longterm effects on root development and architecture over several days. Our analysis revealed trends in nutrient and hormonal signal cross-talk and feedback, including responses that exhibited logic gate behavior, which means that they were triggered only when specific combinations of signals were present. From the data, we developed a multivariate network model comprising the signaling molecules, the early gene expression modulation, and the subsequent changes in root phenotypes. This multivariate network model pinpoints several genes that play key roles in the control of root development and may help understand how eukaryotes manage multifactorial signaling inputs.
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