4.8 Article

A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth

期刊

NUCLEIC ACIDS RESEARCH
卷 49, 期 1, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkaa1073

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资金

  1. Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative [GBMF4557]
  2. National Institutes of Health [1R01GM136842]
  3. Gordon and Betty Moore Foundation

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This study presents a generalizable approach to quantify the relationship between growth rate and titrated changes in gene expression level using CRISPRi. A compounding mutational strategy is effective for generating a monotonic relationship between mutation number and growth rate effect. Molecular barcoding is implemented to detect and correct for mutations that escape CRISPRi targeting machinery, revealing deleterious growth rate effects overlooked by standard methods.
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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