Quantitative design of cell type-specific mRNA stability from microRNA expression data.

Autor: Oesinghaus L; Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, United States., Castillo-Hair S; Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, United States., Ludwig N; Department for Clinical Bioinformatics, Center for Bioinformatics, PharmaScienceHub, Saarland University, Germany., Keller A; Department for Clinical Bioinformatics, Center for Bioinformatics, PharmaScienceHub, Saarland University, Germany., Seelig G; Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, United States.; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA.
Jazyk: angličtina
Zdroj: BioRxiv : the preprint server for biology [bioRxiv] 2024 Oct 28. Date of Electronic Publication: 2024 Oct 28.
DOI: 10.1101/2024.10.28.620728
Abstrakt: 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.
Competing Interests: Competing interests: G.S is a co-founder of Parse Biosciences and an advisor to Deep Genomics and Sanofi.
Databáze: MEDLINE
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