Kemble Harry, Nghe Philippe, Tenaillon Olivier
Infection, Antimicrobials, Modelling, Evolution, INSERM, Unité Mixte de Recherche 1137 Université Paris Diderot, Université Paris Nord Paris France.
École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), UMR CNRS-ESPCI CBI 8231 PSL Research University Paris Cedex 05 France.
Evol Appl. 2019 Aug 11;12(9):1721-1742. doi: 10.1111/eva.12846. eCollection 2019 Oct.
With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (. enzyme activity) and medicine (. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.
随着生物学领域的分子革命，对基因型与表型关系的机制性理解成为可能。最近，DNA合成和测序技术的进步推动了深度突变扫描分析方法的发展，这种方法能够以大规模并行的方式对基因型综合文库的适应性和各种表型进行评分。由此产生的经验性基因型-适应性图谱为预测模型铺平了道路，有可能加快我们从测序数据预测病原体和癌细胞群体行为的能力。除了细胞适应性外，这些分析方法还可以直接量化甚至选择在工业（如酶活性）和医学（如抗体结合）中直接应用的表型。本文综述了大规模并行遗传学的技术基础和最新进展，以及在基因型-表型关系（突变效应分布、上位性）中发现的趋势、其可能的机制基础以及朝着预测遗传学目标前进的未来方向。
