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Riboswitch identification using Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR)

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    Rights statement: © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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https://academic.oup.com/synbio/advance-article/doi/10.1093/synbio/ysz019/5529681
Original languageEnglish
JournalSynthetic Biology
Early online date8 Jul 2019
DOIs
Publication statusE-pub ahead of print - 8 Jul 2019

Abstract

In vitro selection of ligand-responsive ribozymes can identify rare, functional sequences from large libraries. While powerful, key caveats of this approach include lengthy and demanding experimental workflows; unpredictable experimental outcomes and unknown functionality of enriched sequences in vivo. To address the first of these limitations we developed Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR). LigASERR is scalable, amenable to automation and requires less time to implement compared to alternative methods. To improve the predictability of experiments, we modelled the underlying selection process, predicting experimental outcomes based on sequence and population parameters. We applied this new methodology and model to the enrichment of a known, in vitro-selected sequence from a bespoke library. Prior to implementing selection, conditions were optimised and target sequence dynamics accurately predicted for the majority of the experiment. In addition to enriching the target sequence, we identified two new, theophylline-activated ribozymes. Notably, all three sequences yielded riboswitches functional in Escherichia coli, suggesting LigASERR and similar in vitro selection methods can be utilised for generating functional riboswitches in this organism.

    Research areas

  • RNA, Ribozyme, Riboswitch, Synthetic biology, Gene circuit engineering

ID: 105777714