Rapid generation of hypomorphic mutations

Laura L. Arthur, Joyce J. Chung, Preetam Janakirama, Kathryn M. Keefer, Igor Kolotilin, Slavica Pavlovic-Djuranovic, Douglas L. Chalker, Vojislava Grbic, Rachel Green, Rima Menassa, Heather L. True, James B. Skeath and Sergej Djuranovic ()
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Laura L. Arthur: Washington University School of Medicine
Joyce J. Chung: Washington University
Preetam Janakirama: The University of Western Ontario
Kathryn M. Keefer: Washington University School of Medicine
Igor Kolotilin: Scattered Gold Biotechnology Inc. 14 Denali Terrace
Slavica Pavlovic-Djuranovic: Washington University School of Medicine
Douglas L. Chalker: Washington University
Vojislava Grbic: The University of Western Ontario
Rachel Green: Howard Hughes Medical Institute, Johns Hopkins University School of Medicine
Rima Menassa: Science and Technology Branch, Agriculture and Agri-Food Canada
Heather L. True: Washington University School of Medicine
James B. Skeath: Washington University School of Medicine
Sergej Djuranovic: Washington University School of Medicine

Nature Communications, 2017, vol. 8, issue 1, 1-16

Abstract: Abstract Hypomorphic mutations are a valuable tool for both genetic analysis of gene function and for synthetic biology applications. However, current methods to generate hypomorphic mutations are limited to a specific organism, change gene expression unpredictably, or depend on changes in spatial-temporal expression of the targeted gene. Here we present a simple and predictable method to generate hypomorphic mutations in model organisms by targeting translation elongation. Adding consecutive adenosine nucleotides, so-called polyA tracks, to the gene coding sequence of interest will decrease translation elongation efficiency, and in all tested cell cultures and model organisms, this decreases mRNA stability and protein expression. We show that protein expression is adjustable independent of promoter strength and can be further modulated by changing sequence features of the polyA tracks. These characteristics make this method highly predictable and tractable for generation of programmable allelic series with a range of expression levels.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14112

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DOI: 10.1038/ncomms14112

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