bioRxivDevelopmental BiologyPreprint
Temporal Changes in The Proanthocyanidins to Anthocyanins Ratio During Dormancy Associate with Bloom Time Variations in Peach
Protiva Rani DasiD, Md Tabibul IslamiD, Jianyang LiuiD, Zongrang LiuiD, Chris D DardickiD, Sherif M. SherifiD
Abstract
This study provides a thorough exploration of the mechanisms regulating the onset of flowering in peach trees, a process principally governed by bud-dormancy. We applied untargeted metabolomics combined with a comprehensive series of molecular and biochemical experiments to scrutinize the variations in bloom times among different peach cultivars. The impact of exogenous chemical stimuli, specifically ethephon (ET) and abscisic acid (ABA), on bloom times was also evaluated. Our study revealed that the ET-induced delay in bloom time was associated with higher levels of proanthocyanidin (PA) compared to anthocyanins (ACNs) during endodormancy. Furthermore, fluctuations in the PA/ACNs ratio during dormancy demonstrated a strong correlation with the chill requirements and bloom dates of 12 distinct peach genotypes. The research further uncovers the crucial role of ABA in regulating the biosynthesis of PAs and ACNs during peach tree dormancy. Intriguingly, the exogenous application of ABA during endodormancy resulted in a reduction of PA content, leading to an earlier bloom time. We also observed variations in DAM gene expression between early- and late-blooming cultivars. The late-blooming cultivars exhibited higher transcript levels of DAM genes, elevated PA levels, and lower ABA levels compared to their early-blooming counterparts. Importantly, our study proposes PAs and ACNs as quantitative marker metabolites for endo- and ecodormancy phases. This innovative finding paves the way for developing more accurate chill and heat requirement models, thereby enabling a more precise understanding and projection of the impacts of global climate change on the phenology of tree fruit species.
- organism: plant (other)
- assay: metabolomics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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