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在 HEK293 细胞中测量大型丝氨酸整合酶的酶学特性,揭示了其可变性及其对下游报告基因表达的影响。

Measurement of large serine integrase enzymatic characteristics in HEK293 cells reveals variability and influence on downstream reporter expression.

机构信息

Genetics Department, Harvard Medical School, Boston, MA, USA.

Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.

出版信息

FEBS J. 2021 Nov;288(22):6410-6427. doi: 10.1111/febs.16037. Epub 2021 Jun 23.

Abstract

Large serine integrases (LSIs) offer tremendous potential for rapid genetic engineering as well as building biological systems capable of responding to stimuli and integrating information. Currently, there is no unified metric for directly measuring the enzymatic characteristics of LSI function, which hinders evaluation of their suitability to specific applications. Here, we present an experimental protocol for recording DNA recombination in HEK293 cells in real-time through fluorophore expression and software which fits the kinetic data to a model tailored to LSI recombination dynamics. Our model captures the activity of LSIs as three parameters: expression level (K ), catalytic rate (k ), and substrate affinity (K ). The expression level and catalytic rate for phiC31 and Bxb1 varied greatly, suggesting disparate routes to high recombination efficiencies. Moreover, the expression level and substrate affinity jointly impacted downstream reporter expression, potentially by obstructing transcriptional machinery. We validated these observations by swapping between promoters and mutating key recombinase residues and DNA recognition sites to individually modulate each parameter. Our model for identifying key LSI parameters in cellulo provides insight into selecting the optimal recombinase for various applications as well as for guiding the engineering of improved LSIs.

摘要

大型丝氨酸整合酶(LSI)具有巨大的潜力,可以快速进行基因工程,构建能够响应刺激并整合信息的生物系统。目前,没有统一的指标可以直接测量 LSI 功能的酶学特性,这阻碍了对其特定应用适用性的评估。在这里,我们提出了一种通过荧光表达实时记录 HEK293 细胞中 DNA 重组的实验方案,并开发了一款软件,该软件可以将动力学数据拟合到针对 LSI 重组动力学定制的模型中。我们的模型将 LSI 的活性捕获为三个参数:表达水平(K)、催化速率(k)和底物亲和力(K)。phiC31 和 Bxb1 的表达水平和催化速率差异很大,这表明它们具有不同的高重组效率途径。此外,表达水平和底物亲和力共同影响下游报告基因的表达,这可能是通过阻碍转录机制实现的。我们通过在启动子之间交换和突变关键重组酶残基和 DNA 识别位点来单独调节每个参数,验证了这些观察结果。我们在细胞内识别关键 LSI 参数的模型为选择各种应用的最佳重组酶以及指导改进 LSI 的工程设计提供了深入的了解。

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