美国马萨诸塞州总医院Luca Pinello，美国布里格姆妇女医院和哈佛医学院Richard I. Sherwood和Christopher A. Cassa共同合作，近期取得重要工作进展。他们通过基因型和表型结果联合建模改进了碱基编辑变体效应的量化。相关研究成果2024年4月24日在线发表于《自然—遗传学》杂志上。

据介绍，CRISPR碱基编辑筛选能够大规模分析疾病相关变异，然而，不同的效率和精确度阻碍了对变异诱导表型的评估。

研究人员提供了一个实验和计算的综合管道，以改进对碱基编辑筛选中变异效应的估计。研究人员使用报告基因来测量引导 RNA（gRNA）的编辑结果及其表型后果，并引入了带活性归一化的碱基编辑筛选分析（BEAN），这是一种贝叶斯网络，它使用报告基因提供的每条gRNA编辑结果和靶位点染色质可及性来估计变异的影响。BEAN在变异效应量化方面优于现有工具。研究人员利用BEAN确定了改变低密度脂蛋白（LDL）摄取的常见调控变异，涉及到以前未报道过的基因。

此外，通过对LDLR进行饱和碱基编辑，研究人员准确量化了错义变体的致病性，这与英国生物库患者的测量结果一致，并确定了潜在的结构机制。

总之，这项工作提供了一种广泛适用的方法，可提高碱基编辑筛选对疾病相关变异特征描述的能力。

附：英文原文

Title: Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

Author: Ryu, Jayoung, Barkal, Sam, Yu, Tian, Jankowiak, Martin, Zhou, Yunzhuo, Francoeur, Matthew, Phan, Quang Vinh, Li, Zhijian, Tognon, Manuel, Brown, Lara, Love, Michael I., Bhat, Vineel, Lettre, Guillaume, Ascher, David B., Cassa, Christopher A., Sherwood, Richard I., Pinello, Luca

Issue&Volume: 2024-04-24

Abstract: CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization. BEAN is a Bayesian approach for analyzing base editing screens with improved effect size quantification and variant classification. Applied to low-density lipoprotein (LDL)-associated common variants and saturation base editing of LDLR, BEAN identifies new LDL uptake genes and offers insights into variant structure–pathogenicity mechanisms.

DOI: 10.1038/s41588-024-01726-6

Source: https://www.nature.com/articles/s41588-024-01726-6