In silico discovery of repetitive elements as key sequence determinants of 3D genome folding.
In silico discovery of repetitive elements as key sequence determinants of 3D genome folding.
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DOI：
10.1016/j.xgen.2023.100410
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发表时间：
2023-10-11
期刊：
影响因子：
--
通讯作者：
Pollard, Katherine S.
中科院分区：
文献类型：
--
作者：
Gunsalus, Laura M.;Keiser, Michael J.;Pollard, Katherine S.
天然和实验性遗传变异可以修饰DNA环和绝缘边界以调节转录，但序列扰动如何影响全基因组的染色质组织尚不清楚。我们开发了一种深度学习策略来量化任何插入、缺失或取代对染色质接触的影响，并系统地对数百万个合成变体进行评分。虽然大多数遗传操作几乎没有影响，但正如预期的那样，具有CTCF基序和活跃转录的区域是高度敏感的。我们的无偏筛选和随后的靶向实验还指出，非编码RNA基因和几个家族的重复元件作为CTCF基序的DNA序列，对附近的染色质相互作用具有特别大的影响，有时超过CTCF位点的影响，并解释缺乏CTCF的相互作用。我们预计，我们的破坏轨迹可能会引起广泛的兴趣和效用，作为衡量3D基因组敏感性的指标，我们的计算策略可以作为深度学习生物学研究的模板。大规模无偏计算筛选揭示了与基因组折叠相关的元素配对缺失/插入实验解开了必要和足够的序列高分序列包括重复序列（Alu，MIR）和RNA基因（tRNA，snRNA）基因组折叠也对CTCF基序，GC含量和转录敏感Gunsalus et al.使用深度学习模型来筛选人类基因组中序列变化对3D基因组折叠具有特别大的预测影响的区域。他们发现CTCF基序、活跃转录区域以及Alu、hAT-Charlie和SVA重复序列中的序列扰动深刻地影响了染色质相互作用。有针对性的计算实验表明，重复的元素，有时缺乏CTCF基序，提供序列语法控制染色质相互作用。这种无偏的方法暗示特定的重复序列家族是基因组折叠的组成部分。
Natural and experimental genetic variants can modify DNA loops and insulating boundaries to tune transcription, but it is unknown how sequence perturbations affect chromatin organization genome wide. We developed a deep-learning strategy to quantify the effect of any insertion, deletion, or substitution on chromatin contacts and systematically scored millions of synthetic variants. While most genetic manipulations have little impact, regions with CTCF motifs and active transcription are highly sensitive, as expected. Our unbiased screen and subsequent targeted experiments also point to noncoding RNA genes and several families of repetitive elements as CTCF-motif-free DNA sequences with particularly large effects on nearby chromatin interactions, sometimes exceeding the effects of CTCF sites and explaining interactions that lack CTCF. We anticipate that our disruption tracks may be of broad interest and utility as a measure of 3D genome sensitivity, and our computational strategies may serve as a template for biological inquiry with deep learning. Mass, unbiased computational screen reveals elements correlated with genome folding Paired deletion/insertion experiments disentangle necessary and sufficient sequences High-scoring sequences include repeats (Alu, MIR) and RNA genes (tRNA, snRNA) Genome folding is also sensitive to CTCF motifs, GC content, and transcription Gunsalus et al. use a deep learning model to screen the human genome for regions where sequence changes have particularly large predicted effects on 3D genome folding. They find that sequence perturbations in CTCF motifs, actively transcribed regions, and Alu, hAT-Charlie, and SVA repeats profoundly influence chromatin interactions. Targeted computational experiments reveal that repetitive elements, sometimes lacking CTCF motifs, provide sequence grammar governing chromatin interactions. This unbiased approach implicates specific repeat families as integral to genome folding.
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