美国耶鲁大学Monkol Lek等研究人员合作实现疾病相关基因的饱和突变强化功能检测。这一研究成果于2024年9月25日在线发表在国际学术期刊《细胞》上。

研究人员开发出的框架——饱和突变强化功能检测（SMuRF）。其提供了简单且具有成本效益的饱和突变，并配以简化的功能检测，以增强对未解读变异的解释。

将SMuRF应用于神经肌肉疾病基因FKRP和LARGE1时，研究人员为所有可能的编码单核苷酸变异生成了功能评分，有助于解决临床报告中的意义不明确变异。SMuRF还展示了其在预测疾病严重程度、解析关键结构区域以及为开发计算预测器提供训练数据集方面的应用价值。

总体而言，该方法为疾病基因的变异功能解读提供了具有成本效益的途径，能够被标准研究实验室广泛实施。

据悉，在人体遗传学中，对致病遗传变异的解读仍然是一个挑战。目前，深度突变扫描方法的高成本和复杂性是实现疾病相关基因变异的全基因组解析的障碍。

附：英文原文

Title: Saturation mutagenesis-reinforced functional assays for disease-related genes

Author: Kaiyue Ma, Shushu Huang, Kenneth K. Ng, Nicole J. Lake, Soumya Joseph, Jenny Xu, Angela Lek, Lin Ge, Keryn G. Woodman, Katherine E. Koczwara, Justin Cohen, Vincent Ho, Christine L. O’Connor, Melinda A. Brindley, Kevin P. Campbell, Monkol Lek

Issue&Volume: 2024-09-25

Abstract: Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods are obstacles for achieving genome-wide resolution of variants in disease-related genes. Our framework, saturation mutagenesis-reinforced functional assays (SMuRF), offers simple and cost-effective saturation mutagenesis paired with streamlined functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single-nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Overall, our approach enables variant-to-function insights for disease genes in a cost-effective manner that can be broadly implemented by standard research laboratories.

DOI: 10.1016/j.cell.2024.08.047

Source: https://www.cell.com/cell/abstract/S0092-8674(24)00976-0