Oesinghaus Lukas, Castillo-Hair Sebastian, Ludwig Nicole, Keller Andreas, Seelig Georg
Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, United States.
Department for Clinical Bioinformatics, Center for Bioinformatics, PharmaScienceHub, Saarland University, Germany.
bioRxiv. 2024 Oct 28:2024.10.28.620728. doi: 10.1101/2024.10.28.620728.
Limiting expression to target cell types is a longstanding goal in gene therapy, which could be met by sensing endogenous microRNA. However, an unclear association between microRNA expression and activity currently hampers such an approach. Here, we probe this relationship by measuring the stability of synthetic microRNA-responsive 3'UTRs across 10 cell lines in a library format. By systematically addressing biases in microRNA expression data and confounding factors such as microRNA crosstalk, we demonstrate that a straightforward model can quantitatively predict reporter stability purely from expression data. We use this model to design constructs with previously unattainable response patterns across our cell lines. The rules we derive for microRNA expression data selection and processing should apply to microRNA-responsive devices for any environment with available expression data.
将基因表达限制在靶细胞类型上是基因治疗中长期以来的目标，可通过检测内源性微小RNA来实现。然而，目前微小RNA表达与活性之间的关联尚不清楚，这阻碍了这种方法的应用。在此，我们以文库形式测量了10种细胞系中合成的微小RNA响应性3'非翻译区（3'UTR）的稳定性，以此探究这种关系。通过系统地解决微小RNA表达数据中的偏差以及诸如微小RNA串扰等混杂因素，我们证明了一个简单的模型能够仅根据表达数据定量预测报告基因的稳定性。我们利用这个模型设计出在我们的细胞系中具有此前无法实现的响应模式的构建体。我们推导得出的微小RNA表达数据选择和处理规则应适用于任何有可用表达数据环境下的微小RNA响应装置。
