评估变体对疾病影响的高通量测定。

IF 4 3区 医学 Q2 CELL BIOLOGY
Disease Models & Mechanisms Pub Date : 2024-06-01 Epub Date: 2024-06-28 DOI:10.1242/dmm.050573
Kaiyue Ma, Logan O Gauthier, Frances Cheung, Shushu Huang, Monkol Lek
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引用次数: 0

摘要

由于缺乏足够的临床病例报告,如何解读在群体规模测序工作中发现的大量罕见遗传变异,并破译它们与人类健康和疾病之间的关联,是一项严峻的挑战。深度基因突变扫描(DMS)是克服这一问题的一个很有前景的途径,这是一种在模型细胞系中引入和评估大规模基因变异的方法。DMS 允许对变异进行无偏见的调查,包括那些在临床报告中未发现的变异,从而改善罕见病诊断。目前,限制 DMS 充分发挥潜力的主要障碍是缺乏针对疾病机制的功能检测方法。因此,我们探讨了适合研究广泛疾病机制的高通量功能方法。我们特别关注那些不需要机器人或自动化,而是利用精心设计的分子工具将生物机制转化为易于检测的信号(如细胞存活率、荧光或耐药性)的方法。在这里,我们的目标是弥合疾病相关测定与将其整合到 DMS 框架之间的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-throughput assays to assess variant effects on disease.

Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.

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来源期刊
Disease Models & Mechanisms
Disease Models & Mechanisms 医学-病理学
CiteScore
6.60
自引率
7.00%
发文量
203
审稿时长
6-12 weeks
期刊介绍: Disease Models & Mechanisms (DMM) is an online Open Access journal focusing on the use of model systems to better understand, diagnose and treat human disease.
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