微扰基因表达谱在药物发现中的应用——从作用机制到定量建模

B. Szalai, D. Veres
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引用次数: 0

摘要

药物靶点、化合物效应和疾病表型的高维表征对于提高药物发现的效率至关重要。高通量基因表达测量是用于生物表型的这种系统级分析的最常用的数据获取方法之一。RNA测序可以在全基因组范围内量化转录物丰度,最近甚至在单细胞水平上也是如此。然而,由于基因表达变化可能是表型改变的原因和结果,转录组测量的正确、机制解释变得复杂。扰动基因表达谱,即在遗传或化学扰动后测量基因表达,可以通过将因果扰动与其基因表达结果直接联系起来,帮助克服这些问题。在这篇综述中,我们讨论了主要的大规模扰动基因表达谱数据集,以及它们在药物发现过程中的应用,包括作用鉴定机制、药物再利用、通路活性分析和定量建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of perturbation gene expression profiles in drug discovery—From mechanism of action to quantitative modelling
High dimensional characterization of drug targets, compound effects and disease phenotypes are crucial for increased efficiency of drug discovery. High-throughput gene expression measurements are one of the most frequently used data acquisition methods for such a systems level analysis of biological phenotypes. RNA sequencing allows genome wide quantification of transcript abundances, recently even on the level of single cells. However, the correct, mechanistic interpretation of transcriptomic measurements is complicated by the fact that gene expression changes can be both the cause and the consequence of altered phenotype. Perturbation gene expression profiles, where gene expression is measured after a genetic or chemical perturbation, can help to overcome these problems by directly connecting the causal perturbations to their gene expression consequences. In this Review, we discuss the main large scale perturbation gene expression profile datasets, and their application in the drug discovery process, covering mechanisms of action identification, drug repurposing, pathway activity analysis and quantitative modelling.
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