A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth.
A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth.
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DOI：
10.1093/nar/gkaa1073
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发表时间：
2021-01-11
影响因子：
14.9
通讯作者：
Reynolds KA
中科院分区：
文献类型：
--
作者：
Mathis AD;Otto RM;Reynolds KA
缺乏高通量技术进行滴定，基因特异性的表达变化限制了我们的理解基因表达和细胞表型之间的关系。在这里，我们提出了一个可推广的方法来量化生长率作为滴定的基因表达水平的变化的函数。该方法通过使用一系列调节基因表达的突变单向导RNA（sgRNA）进行CRISPRi来工作。为了评估sgRNA突变策略，我们构建了5927个sgRNA的文库，靶向大肠杆菌MG 1655中的88个基因，并测量了对生长速率的影响。我们发现，复合突变策略，通过该策略，突变被递增地添加到sgRNA中，提供了一种直接的方式来产生突变数量和生长速率效应之间的单调和渐变关系。我们还实施了分子条形码，以检测和纠正“逃避”CRISPRi靶向机制的突变;这种策略揭示了被忽略逃避者的标准方法所掩盖的有害生长速率效应。最后，我们进行了受控的环境变化，并观察到许多基因与环境的相互作用在最大敲低的极限下完全未被检测到，而是在中间表达扰动强度下表现出来。总的来说，我们的工作提供了一个实验平台，用于量化基因表达变异的表型反应。
A lack of high-throughput techniques for making titrated, gene-specific changes in expression limits our understanding of the relationship between gene expression and cell phenotype. Here, we present a generalizable approach for quantifying growth rate as a function of titrated changes in gene expression level. The approach works by performing CRISPRi with a series of mutated single guide RNAs (sgRNAs) that modulate gene expression. To evaluate sgRNA mutation strategies, we constructed a library of 5927 sgRNAs targeting 88 genes in Escherichia coli MG1655 and measured the effects on growth rate. We found that a compounding mutational strategy, through which mutations are incrementally added to the sgRNA, presented a straightforward way to generate a monotonic and gradated relationship between mutation number and growth rate effect. We also implemented molecular barcoding to detect and correct for mutations that ‘escape’ the CRISPRi targeting machinery; this strategy unmasked deleterious growth rate effects obscured by the standard approach of ignoring escapers. Finally, we performed controlled environmental variations and observed that many gene-by-environment interactions go completely undetected at the limit of maximum knockdown, but instead manifest at intermediate expression perturbation strengths. Overall, our work provides an experimental platform for quantifying the phenotypic response to gene expression variation.
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影响因子：
4.5
作者：
Perfeito L;Ghozzi S;Berg J;Schnetz K;Lässig M
通讯作者：
Lässig M
DOI：
10.1007/978-1-4939-2687-9_23
发表时间：
2015
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
Hawkins JS;Wong S;Peters JM;Almeida R;Qi LS
通讯作者：
Qi LS
影响因子：
14.9
作者：
Guiziou S;Sauveplane V;Chang HJ;Clerté C;Declerck N;Jules M;Bonnet J
通讯作者：
Bonnet J
影响因子：
48
作者：
Butland, Gareth;Babu, Mohan;Emili, Andrew
通讯作者：
Emili, Andrew
影响因子：
14.8
作者：
Meyer, Adam J.;Segall-Shapiro, Thomas H.;Voigt, Christopher A.
通讯作者：
Voigt, Christopher A.