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dCas9 调节剂可中和 CRISPRi 回路中的竞争。

dCas9 regulator to neutralize competition in CRISPRi circuits.

机构信息

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

出版信息

Nat Commun. 2021 Mar 16;12(1):1692. doi: 10.1038/s41467-021-21772-6.

DOI:10.1038/s41467-021-21772-6
PMID:33727557
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7966764/
Abstract

CRISPRi-mediated gene regulation allows simultaneous control of many genes. However, highly specific sgRNA-promoter binding is, alone, insufficient to achieve independent transcriptional regulation of multiple targets. Indeed, due to competition for dCas9, the repression ability of one sgRNA changes significantly when another sgRNA becomes expressed. To solve this problem and decouple sgRNA-mediated regulatory paths, we create a dCas9 concentration regulator that implements negative feedback on dCas9 level. This allows any sgRNA to maintain an approximately constant dose-response curve, independent of other sgRNAs. We demonstrate the regulator performance on both single-stage and layered CRISPRi-based genetic circuits, zeroing competition effects of up to 15-fold changes in circuit I/O response encountered without the dCas9 regulator. The dCas9 regulator decouples sgRNA-mediated regulatory paths, enabling concurrent and independent regulation of multiple genes. This allows predictable composition of CRISPRi-based genetic modules, which is essential in the design of larger scale synthetic genetic circuits.

摘要

CRISPRi 介导的基因调控允许同时控制多个基因。然而,高度特异性 sgRNA-启动子结合本身不足以实现多个靶标的独立转录调控。事实上,由于 dCas9 的竞争,当另一个 sgRNA 表达时,一个 sgRNA 的抑制能力会发生显著变化。为了解决这个问题并解耦 sgRNA 介导的调控途径,我们创建了一种 dCas9 浓度调节剂,它对 dCas9 水平实施负反馈。这使得任何 sgRNA 都能保持大致恒定的剂量反应曲线,而不受其他 sgRNA 的影响。我们在单级和分层 CRISPRi 遗传回路中展示了该调节剂的性能,消除了没有 dCas9 调节剂时遇到的高达 15 倍的回路 I/O 响应竞争效应。dCas9 调节剂解耦了 sgRNA 介导的调控途径,实现了多个基因的并发和独立调控。这允许基于 CRISPRi 的遗传模块的可预测组成,这对于更大规模合成遗传回路的设计至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7427/7966764/bd4080a84d45/41467_2021_21772_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7427/7966764/e582d556cc3b/41467_2021_21772_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7427/7966764/892f9c4a0d68/41467_2021_21772_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7427/7966764/bd4080a84d45/41467_2021_21772_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7427/7966764/e582d556cc3b/41467_2021_21772_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7427/7966764/892f9c4a0d68/41467_2021_21772_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7427/7966764/bd4080a84d45/41467_2021_21772_Fig3_HTML.jpg

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