Gunsalus Laura M, Keiser Michael J, Pollard Katherine S
Gladstone Institutes, San Francisco, CA, USA.
Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA.
Cell Genom. 2023 Sep 25;3(10):100410. doi: 10.1016/j.xgen.2023.100410. eCollection 2023 Oct 11.
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.
自然和实验性遗传变异可修饰DNA环和绝缘边界以调节转录，但尚不清楚序列扰动如何在全基因组范围内影响染色质组织。我们开发了一种深度学习策略，以量化任何插入、缺失或替换对染色质接触的影响，并系统地对数百万个合成变异进行评分。正如预期的那样，虽然大多数基因操作影响不大，但具有CTCF基序和活跃转录的区域高度敏感。我们的无偏筛选及后续的靶向实验还指出，非编码RNA基因和几个重复元件家族作为无CTCF基序的DNA序列，对附近染色质相互作用有特别大的影响，有时超过CTCF位点的影响，并解释了缺乏CTCF的相互作用。我们预计，我们的破坏轨迹作为一种3D基因组敏感性的度量可能具有广泛的研究兴趣和用途，并且我们的计算策略可作为深度学习生物学探究的模板。
