Establishing a Multivariate Model for Predictable Antisense RNA-Mediated Repression
Establishing a Multivariate Model for Predictable Antisense RNA-Mediated Repression
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DOI：
10.1021/acssynbio.8b00227
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发表时间：
2019-01-01
影响因子：
4.7
通讯作者：
Moon, Tae Seok
中科院分区：
文献类型：
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作者：
Lee, Young Je;Kim, Soo-Jung;Moon, Tae Seok
我们对RNA折叠和功能的理解的最新进展促进了使用调控RNA如合成反义RNA（asRNA）来调节基因表达。然而，尽管有简单和普遍的互补规则，但由于天然asRNA介导的基因调控的内在复杂性，可预测的asRNA介导的抑制仍然具有挑战性。为了解决这个问题，我们提出了一个多变量模型，基于复合物形成的自由能的变化（Δ G（CF））和靶结合区的错配百分比，它可以预测合成的asRNA介导的抑制效率在不同的情况下。首先，设计并测试了69种与多个靶mRNA结合的asRNA，以创建预测模型。其次，我们证明了相同的模型可以有效预测质粒和染色体中靶基因的阻遏。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的asRNA，以证明对细胞生长的可调控制。第四，我们在两种不同的生物技术上重要的生物体中测试和验证了相同的模型：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，GC含量和基因表达水平的存在下，重新定义的条件下，多变量模型应用于准确的预测。总共测试了434种不同的菌株-asRNA组合，在各种情况下验证了预测模型，包括多个靶基因和生物体。这项研究中提出的结果是实现asRNA介导的抑制的可预测性的重要一步。
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 (Delta G(CF)) 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.