Analysis of the Root System Architecture of Arabidopsis Provides a Quantitative Readout of Crosstalk between Nutritional Signals

As plant roots forage the soil for food and water, they translate a multifactorial input of environmental stimuli into a multifactorial developmental output that manifests itself as root system architecture (RSA). Our current understanding of the underlying regulatory network is limited because root...

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Bibliographic Details
Published in:The Plant cell 2014-04, Vol.26 (4), p.1480-1496
Main Authors: Kellermeier, Fabian, Armengaud, Patrick, Seditas, Triona J., Danku, John, Salt, David E., Amtmann, Anna
Format: Article
Language:English
Subjects:
Architecture
Datasets
Genes
Life Sciences
Nitrates
Nutrient nutrient interactions
Nutritional deficiencies
Plant nutrition
Plant roots
Plants
Starvation
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Summary:As plant roots forage the soil for food and water, they translate a multifactorial input of environmental stimuli into a multifactorial developmental output that manifests itself as root system architecture (RSA). Our current understanding of the underlying regulatory network is limited because root responses have traditionally been studied separately for individual nutrient deficiencies. In this study, we quantified 13 RSA parameters of Arabidopsis thaliana in 32 binary combinations of N, P, K, S, and light. Analysis of variance showed that each RSA parameter was determined by a typical pattern of environmental signals and their interactions. P caused the most important single-nutrient effects, while N-effects were strongly light dependent. Effects of K and S occurred mostly through nutrient interactions in paired or multiple combinations. Several RSA parameters were selected for further analysis through mutant phenotyping, which revealed combinations of transporters, receptors, and kinases acting as signaling modules in K-N interactions. Furthermore, nutrient response profiles of individual RSA features across NPK combinations could be assigned to transcriptionally coregulated clusters of nutrient-responsive genes in the roots and to ionome patterns in the shoots. The obtained data set provides a quantitative basis for understanding how plants integrate multiple nutritional stimuli into complex developmental programs.
ISSN:1040-4651
1532-298X
DOI:10.1105/tpc.113.122101