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

IF 3.5 2区 农林科学 Q1 FORESTRY
Takanori Saito, Shanshan Wang, Katsuya Ohkawa, Hitoshi Ohara, Satoru Kondo
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引用次数: 0

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.

利用小型数据集进行深度学习,预测染色质重塑对苹果腋芽冬季休眠的影响。
表观遗传学变化是草本植物和树种(包括芽休眠)累积冷识别的细胞记忆。然而,大多数研究都是讨论组蛋白标记方面的染色质结构预测。在本研究中,我们调查了真正染色质的结构动态,以确定植物如何在芽休眠的初始阶段识别长时间的寒冷。染色质结构和转录变化分析采用的是'富士'苹果的无性腋芽,它表现出典型的低温依赖性休眠诱导,而非光周期休眠诱导。分析结果使用深度学习模型进行整合,并使用统计模型(包括贝叶斯估计)进行解释。虽然我们的模型是利用两个时间点的小数据集构建的,但排除了随机变化引起的染色质重塑。我们预测了大多数核小体结构变化对转录变化的参与,以及冷驱动昼夜节律依赖途径对顺式调控元件移动性调控的关键贡献。这些发现可能有助于开发潜在的遗传目标,以培育花芽休眠较少的物种,克服全球变暖带来的短冬影响。我们的人工智能概念可以利用小数据集改进表观遗传学分析,特别是在基因组数据库不成熟的非模式植物中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Tree physiology
Tree physiology 农林科学-林学
CiteScore
7.10
自引率
7.50%
发文量
133
审稿时长
1 months
期刊介绍: Tree Physiology promotes research in a framework of hierarchically organized systems, measuring insight by the ability to link adjacent layers: thus, investigated tree physiology phenomenon should seek mechanistic explanation in finer-scale phenomena as well as seek significance in larger scale phenomena (Passioura 1979). A phenomenon not linked downscale is merely descriptive; an observation not linked upscale, might be trivial. Physiologists often refer qualitatively to processes at finer or coarser scale than the scale of their observation, and studies formally directed at three, or even two adjacent scales are rare. To emphasize the importance of relating mechanisms to coarser scale function, Tree Physiology will highlight papers doing so particularly well as feature papers.
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