Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel.
The Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv, Israel.
PLoS Comput Biol. 2024 Jun 7;20(6):e1012214. doi: 10.1371/journal.pcbi.1012214. eCollection 2024 Jun.
CRISPR is a gene editing technology which enables precise in-vivo genome editing; but its potential is hampered by its relatively low specificity and sensitivity. Improving CRISPR's on-target and off-target effects requires a better understanding of its mechanism and determinants. Here we demonstrate, for the first time, the chromosomal 3D spatial structure's association with CRISPR's cleavage efficiency, and its predictive capabilities. We used high-resolution Hi-C data to estimate the 3D distance between different regions in the human genome and utilized these spatial properties to generate 3D-based features, characterizing each region's density. We evaluated these features based on empirical, in-vivo CRISPR efficiency data and compared them to 425 features used in state-of-the-art models. The 3D features ranked in the top 13% of the features, and significantly improved the predictive power of LASSO and xgboost models trained with these features. The features indicated that sites with lower spatial density demonstrated higher efficiency. Understanding how CRISPR is affected by the 3D DNA structure provides insight into CRISPR's mechanism in general and improves our ability to correctly predict CRISPR's cleavage as well as design sgRNAs for therapeutic and scientific use.
CRISPR 是一种基因编辑技术，可实现精确的体内基因组编辑；但其潜在功能受到相对较低的特异性和敏感性的限制。要提高 CRISPR 的靶标和脱靶效应，需要更好地了解其机制和决定因素。在这里，我们首次证明了染色体 3D 空间结构与 CRISPR 切割效率及其预测能力之间的关联。我们使用高分辨率 Hi-C 数据来估计人类基因组中不同区域之间的 3D 距离，并利用这些空间特性生成基于 3D 的特征，以表征每个区域的密度。我们根据经验性的体内 CRISPR 效率数据评估了这些特征，并将其与最先进模型中使用的 425 个特征进行了比较。3D 特征在所有特征中排名前 13%，并且显著提高了使用这些特征训练的 LASSO 和 xgboost 模型的预测能力。这些特征表明，空间密度较低的位点具有更高的效率。了解 CRISPR 如何受到 3D DNA 结构的影响，可以深入了解 CRISPR 的一般机制，并提高我们正确预测 CRISPR 切割以及设计用于治疗和科学用途的 sgRNA 的能力。
