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Combinatorial interaction network of transcriptomic and phenotypic responses to nitrogen and hormones in the Arabidopsis thaliana root
Author(s) -
Daniela Ristova,
Clément Carré,
Marjorie Pervent,
Anna Medici,
Grace J. Kim,
Domenica Scalia,
Sandrine Ruffel,
Kenneth D. Birnbaum,
Benoı̂t Lacombe,
Wolfgang Busch,
Gloria M. Coruzzi,
Gabriel Krouk
Publication year - 2016
Publication title -
science signaling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.659
H-Index - 154
eISSN - 1937-9145
pISSN - 1945-0877
DOI - 10.1126/scisignal.aaf2768
Subject(s) - arabidopsis thaliana , transcriptome , arabidopsis , biology , hormone , phenotype , nutrient , plant hormone , computational biology , botany , microbiology and biotechnology , gene , genetics , biochemistry , ecology , gene expression , mutant
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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