Deep learning with a small dataset predicts chromatin remodelling contribution to winter dormancy of apple axillary buds

Abstract

Abstract Epigenetic changes serve as a cellular memory for cumulative cold recognition in both herbaceous and tree species, including bud dormancy. However, most studies have discussed predicted chromatin structure with respect to histone marks. In the present study, we investigated the structural dynamics of bona fide chromatin to determine how plants recognize prolonged chilling during the initial stage of bud dormancy. The vegetative axillary buds of the ‘Fuji’ apple, which shows typical low temperature-dependent, but not photoperiod, dormancy induction, were used for the chromatin structure and transcriptional change analyses. The results were integrated using a deep-learning model and interpreted using statistical models, including Bayesian estimation. Although our model was constructed using a small dataset of two time points, chromatin remodelling due to random changes was excluded. The involvement of most nucleosome structural changes in transcriptional changes and the pivotal contribution of cold-driven circadian rhythm-dependent pathways regulated by the mobility of cis-regulatory elements were predicted. These findings may help to develop potential genetic targets for breeding species with less bud dormancy to overcome the effects of short winters during global warming. Our artificial intelligence concept can improve epigenetic analysis using a small dataset, especially in non-model plants with immature genome databases.