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Characterization of New Transcripts Enriched in the Mouse Retina and Identification of Candidate Retinal Disease Genes

¹From the Maisonneuve-Rosemont Hospital, Montreal, Quebec, Canada; the ⁴Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada; and the ²Departments of Ophthalmology and Visual Sciences and ³Human Genetics, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.

PURPOSE. Most retinal disease genes are preferentially expressed in photoreceptors, the light-sensitive cells involved in phototransduction. In addition, some of the genes linked to retinal diseases are essential for normal retinal development. The goal of this study was to identify new transcripts enriched in photoreceptors involved in retinal development or diseases.

METHODS. To isolate uncharacterized retinal transcripts, the bioinformatic method Digital Differential Display (DDD) was used. RNA in situ hybridization was used to characterize gene-expression patterns.

RESULTS. Twenty-seven mouse ESTs highly represented in retinal libraries were identified. Eight ESTs were predominantly expressed in photoreceptors and/or in the retinal pigment epithelium (RPE), whereas transcripts for other ESTs were detected more ubiquitously in the retinal cells or abundantly in ganglion cells and/or the inner nuclear layer. Mapping of the corresponding human orthologues of the photoreceptor/RPE–enriched genes revealed that two of them are candidate disease genes for retinitis pigmentosa, loci RP22 and RP28. Both of these are predominantly expressed in rod photoreceptors. The candidate RP22 gene codes for a putative transmembrane protein showing homology to Cln8 (ceroid lipofuscinosis, neuronal 8), in which gene mutations are associated with photoreceptors degeneration in mice. Also identified were two genes expressed in photoreceptors that are candidate disease genes for recessive Bardet-Biedl syndrome type 3 (BBS3) and recessive ataxia with RP (AXPC1).

CONCLUSIONS. This study demonstrates how bioinformatic analysis can be used to identify novel tissue-specific genes relevant to development and diseases.

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