High-throughput identification of synthetic riboswitches by barcode-free amplicon-sequencing in human cells

Synthetic riboswitches mediating ligand-dependent RNA cleavage or splicing-modulation represent elegant tools to control gene expression in various applications, including next-generation gene therapy. However, due to the limited understanding of context-dependent structure–function relationships, t...

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Veröffentlicht in:Nature communications 2020-02, Vol.11 (1), p.714-12, Article 714
Hauptverfasser: Strobel, Benjamin, Spöring, Maike, Klein, Holger, Blazevic, Dragica, Rust, Werner, Sayols, Sergi, Hartig, Jörg S., Kreuz, Sebastian
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Sprache:eng
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Zusammenfassung:Synthetic riboswitches mediating ligand-dependent RNA cleavage or splicing-modulation represent elegant tools to control gene expression in various applications, including next-generation gene therapy. However, due to the limited understanding of context-dependent structure–function relationships, the identification of functional riboswitches requires large-scale-screening of aptamer-effector-domain designs, which is hampered by the lack of suitable cellular high-throughput methods. Here we describe a fast and broadly applicable method to functionally screen complex riboswitch libraries (~1.8 × 10 4 constructs) by cDNA-amplicon-sequencing in transiently transfected and stimulated human cells. The self-barcoding nature of each construct enables quantification of differential mRNA levels without additional pre-selection or cDNA-manipulation steps. We apply this method to engineer tetracycline- and guanine-responsive ON- and OFF-switches based on hammerhead, hepatitis-delta-virus and Twister ribozymes as well as U1-snRNP polyadenylation-dependent RNA devices. In summary, our method enables fast and efficient high-throughput riboswitch identification, thereby overcoming a major hurdle in the development cascade for therapeutically applicable gene switches. Riboswitches can mediate ligand-dependent RNA cleavage and splicing to control gene expression. Here the authors present a method to functionally screen large libraries and identify functional variants.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-14491-x