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Enhancer trapping in zebrafish using the Sleeping Beauty transposon

BioMed Central
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Abstract ral ss BioMed CentBMC Genomics Open AcceResearch article Enhancer trapping in zebrafish using the Sleeping Beauty transposon Darius Balciunas1, Ann E Davidson1,2, Sridhar Sivasubbu1, Spencer B Hermanson1, Zachary Welle1 and Stephen C Ekker*1 Address: 1Arnold and Mabel Beckman Center for Transposon Research, Department of Genetics, Cell Biology and Development, University of Minnesota, 6-160 Jackson Hall, 321 Church St SE, Minneapolis, MN-55455, USA and 2Molecular, Cellular, Developmental Biology and Genetics Graduate Program, University of Minnesota, USA Email: Darius Balciunas - [email protected]; Ann E Davidson - [email protected]; Sridhar Sivasubbu - [email protected]; Spencer B Hermanson - [email protected]; Zachary Welle - [email protected]; Stephen C Ekker* - [email protected] * Corresponding author Abstract Background: Among functional elements of a metazoan gene, enhancers are particularly difficult to find and annotate. Pioneering experiments in Drosophila have demonstrated the value of enhancer "trapping" using an invertebrate to address this functional genomics problem. Results: We modulated a Sleeping Beauty transposon-based transgenesis cassette to establish an enhancer trapping technique for use in a vertebrate model system, zebrafish Danio rerio. We established 9 lines of zebrafish with distinct tissue- or organ-specific GFP expression patterns from 90 founders that produced GFP-expressing progeny. We have molecularly characterized these lines and show that in each line, a specific GFP expression pattern is due to a single transposition event. Many of the insertions are into introns of zebrafish genes predicted in the current genome assembly. We have identified both previously characterized as well as novel expression patterns from this screen. For example, the ET7 line harbors a transposon insertion near the mkp3 locus and expresses GFP in the midbrain-hindbrain boundary, forebrain and the ventricle, matching a subset of the known

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