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Establishing a Multivariate Model for Predictable Antisense RNA-Mediated Repression.
ACS Synthetic Biology ( IF 4.7 ) Pub Date : 2018-12-18 , DOI: 10.1021/acssynbio.8b00227
Young Je Lee 1 , Soo-Jung Kim 1 , Matthew B Amrofell 1 , Tae Seok Moon 1
Affiliation  

Recent advances in our understanding of RNA folding and functions have facilitated the use of regulatory RNAs such as synthetic antisense RNAs (asRNAs) to modulate gene expression. However, despite the simple and universal complementarity rule, predictable asRNA-mediated repression is still challenging due to the intrinsic complexity of native asRNA-mediated gene regulation. To address this issue, we present a multivariate model, based on the change in free energy of complex formation (Δ GCF) and percent mismatch of the target binding region, which can predict synthetic asRNA-mediated repression efficiency in diverse contexts. First, 69 asRNAs that bind to multiple target mRNAs were designed and tested to create the predictive model. Second, we showed that the same model is effective predicting repression of target genes in both plasmids and chromosomes. Third, using our model, we designed asRNAs that simultaneously modulated expression of a toxin and its antitoxin to demonstrate tunable control of cell growth. Fourth, we tested and validated the same model in two different biotechnologically important organisms: Escherichia coli Nissle 1917 and Bacillus subtilis 168. Last, multiple parameters, including target locations, the presence of an Hfq binding site, GC contents, and gene expression levels, were revisited to define the conditions under which the multivariate model should be used for accurate prediction. Together, 434 different strain-asRNA combinations were tested, validating the predictive model in a variety of contexts, including multiple target genes and organisms. The result presented in this study is an important step toward achieving predictable tunability of asRNA-mediated repression.

中文翻译:

建立可预测的反义RNA介导的抑制的多元模型。

我们对RNA折叠和功能的了解的最新进展促进了调节RNA的使用,例如合成反义RNA(asRNA)来调节基因表达。然而,尽管简单和通用的互补性规则,由于天然asRNA介导的基因调控的内在复杂性,可预测的asRNA介导的阻遏仍然具有挑战性。为了解决这个问题,我们基于复合物形成的自由能(ΔGCF)的变化和目标结合区域的错配百分比,提出了一个多变量模型,该模型可以预测在不同情况下合成的asRNA介导的阻遏效率。首先,设计并测试了与多种靶标mRNA结合的69种asRNA,以创建预测模型。第二,我们证明了相同的模型可以有效地预测质粒和染色体中靶基因的表达。第三,使用我们的模型,我们设计了同时调节毒素及其抗毒素表达以证明可调节的细胞生长控制的asRNA。第四,我们在两种重要的生物技术上重要的生物体中测试并验证了同一模型:大肠杆菌Nissle 1917和枯草芽孢杆菌168。最后,多个参数包括目标位置,Hfq结合位点的存在,GC含量和基因表达水平,再次对定义多元模型进行准确预测的条件进行了重新定义。总共测试了434种不同的品系-asRNA组合,从而在多种情况下验证了预测模型,包括多个目标基因和生物。
更新日期:2018-12-05
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