Characterization of New Transcripts Enriched in the Mouse Retina and Identification of Candidate Retinal Disease Genes
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".