bioRxivPlant BiologyPreprint
Delay in flowering time in Col-0 under water deficit and in ddc triple knockout mutant
Abstract
This study addresses the role of cytosine methylation in the fine tuning of flowering time under conditions of water deficit in Arabidopsis thaliana. For this purpose, the drm1 drm2 cmt3 triple methylation mutant in the Col-0 background was used. The plants were grown under long day conditions with water deficit induced by cessation of watering starting at 12 days after seeding. Col-0 showed 1 day delay in flowering as a result of the treatment. ddc on the other hand, showed 2 days of delay regardless of the experimental conditions. We found that the two b-box domain proteins BBX16/COL7 and BBX17/COL8 became overexpressed in the ddc background and in Col-0 under water deficit at 24 days after seeding. Additionally, the NF-YA2 transcription factor became correspondingly down-regulated. Our results suggest a model, where BBX16/COL7 and BBX17/COL8 interact with CONSTANS to delay the induction of FT under long day conditions. NF-YA2, which is also recognized as a promoter of FT expression, with its down-regulation causes additional delay of FT induced flowering. In the Col-0 background, the weak FRIGIDA allele fails to induce sufficient expression of FLC for additional suppression of FT. The plants overcome easily the BBX/NF-YA inhibition resulting in a relatively small delay in flowering. The expression patterns of the three genes suggest involvement of cytosine methylation in their regulation, however no differential methylation could be found in cis that can explain these effects. The results therefore, suggest a trans acting mechanism. Considering that the activities of BBX16/COL7 and BBX17/COL8 in different physiological conditions are not elucidated, this paper provides a background for future experiments targeting the role of these genes in the fine tuning of flowering time in Arabidopsis thaliana.
- organism: Arabidopsis
- assay: DNA methylation
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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