Effective heritable gene knockdown in zebrafish using synthetic microRNAs
Jean Giacomotto (),
Silke Rinkwitz and
Thomas S. Becker ()
Additional contact information
Jean Giacomotto: Brain and Mind Research Institute, Sydney Medical School, University of Sydney
Silke Rinkwitz: Brain and Mind Research Institute, Sydney Medical School, University of Sydney
Thomas S. Becker: Brain and Mind Research Institute, Sydney Medical School, University of Sydney
Nature Communications, 2015, vol. 6, issue 1, 1-11
Abstract:
Abstract Although zebrafish is used to model human diseases through mutational and morpholino-based knockdown approaches, there are currently no robust transgenic knockdown tools. Here we investigate the knockdown efficiency of three synthetic miRNA-expressing backbones and show that these constructs can downregulate a sensor transgene with different degrees of potency. Using this approach, we reproduce spinal muscular atrophy (SMA) in zebrafish by targeting the smn1 gene. We also generate different transgenic lines, with severity and age of onset correlated to the level of smn1 inhibition, recapitulating for the first time the different forms of SMA in zebrafish. These lines are proof-of-concept that miRNA-based approaches can be used to generate potent heritable gene knockdown in zebrafish.
Date: 2015
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.nature.com/articles/ncomms8378 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8378
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/ncomms8378
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().