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Journal of Biomedicine and Biotechnology
Volume 2012, Article ID 350352, 7 pages
http://dx.doi.org/10.1155/2012/350352
Research Article

In Vivo Testing of MicroRNA-Mediated Gene Knockdown in Zebrafish

1School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand
2Green Lane Paediatric and Congenital Cardiac Service, Auckland City Hospital/Starship Children’s Hospital, Auckland, New Zealand
3Department of Obstetrics and Gynaecology, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
4LabPLUS, Auckland City Hospital, P.O. Box 110031, Auckland Mail Centre, Auckland 1142, New Zealand

Received 15 September 2011; Revised 25 November 2011; Accepted 25 November 2011

Academic Editor: Kurt Bürki

Copyright © 2012 Ivone Un San Leong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The zebrafish (Danio rerio) has become an attractive model for human disease modeling as there are a large number of orthologous genes that encode similar proteins to those found in humans. The number of tools available to manipulate the zebrafish genome is limited and many currently used techniques are only effective during early development (such as morpholino-based antisense technology) or it is phenotypically driven and does not offer targeted gene knockdown (such as chemical mutagenesis). The use of RNA interference has been met with controversy as off-target effects can make interpreting phenotypic outcomes difficult; however, this has been resolved by creating zebrafish lines that contain stably integrated miRNA constructs that target the desired gene of interest. In this study, we show that a commercially available miRNA vector system with a mouse-derived miRNA backbone is functional in zebrafish and is effective in causing eGFP knockdown in a transient in vivo eGFP sensor assay system. We chose to apply this system to the knockdown of transcripts that are implicated in the human cardiac disorder, Long QT syndrome.