Cyclic nucleotides have been shown to play important signaling roles in many physiological processes in plants including photosynthesis and defence. Despite this, little is known about cyclic nucleotide-dependent signaling mechanisms in plants since the downstream target proteins remain unknown. This is largely due to the fact that bioinformatics searches fail to identify plant homologs of protein kinases and phosphodiesterases that are the main targets of cyclic nucleotides in animals. An affinity purification technique was used to identify cyclic nucleotide binding proteins in Arabidopsis thaliana. The identified proteins were subjected to a computational analysis that included a sequence, transcriptional co-expression and functional annotation analysis in order to assess their potential role in plant cyclic nucleotide signaling. A total of twelve cyclic nucleotide binding proteins were identified experimentally including key enzymes in the Calvin cycle and photorespiration pathway. Importantly, eight of the twelve proteins were shown to contain putative cyclic nucleotide binding domains. Moreover, the identified proteins are post-translationally modified by nitric oxide, transcriptionally co-expressed and annotated to function in hydrogen peroxide signaling and the defence response. The activity of one of these proteins, GLYGOLATE OXIDASE 1, a photorespiratory enzyme that produces hydrogen peroxide in response to Pseudomonas, was shown to be repressed by a combination of cGMP and nitric oxide treatment. We propose that the identified proteins function together as points of cross-talk between cyclic nucleotide, nitric oxide and reactive oxygen species signaling during the defence response.
The arabidopsis cyclic nucleotide interactome
Lara Donaldson,S. Meier,C. Gehring
Published 2016 in Cell Communication and Signaling
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- Publication year
2016
- Venue
Cell Communication and Signaling
- Publication date
2016-05-11
- Fields of study
Biology, Medicine, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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