In vivo function of the orphan nuclear receptor NR2E3 in establishing photoreceptor identity during mammalian retinal development
Bibliographic record
Abstract
Rod and cone photoreceptors in mammalian retina are generated from common pool(s) of neuroepithelial progenitors. NRL, CRX and NR2E3 are key transcriptional regulators that control photoreceptor differentiation. Mutations in NR2E3, a rod-specific orphan nuclear receptor, lead to loss of rods, increased density of S-cones and supernormal S-cone-mediated vision in humans. To better understand its in vivo function, NR2E3 was expressed ectopically in the Nrl-/- retina, where post-mitotic precursors fated to be rods develop into functional S-cones similar to the human NR2E3 disease. Expression of NR2E3 in the Nrl-/- retina completely suppressed cone differentiation and resulted in morphologically rod-like photoreceptors, which were however not functional. Gene profiling of FACS-purified photoreceptors confirmed the role of NR2E3 as a strong suppressor of cone genes but an activator of only a subset of rod genes (including rhodopsin) in vivo. Ectopic expression of NR2E3 in cone precursors and differentiating S-cones of wild-type retina also generated rod-like cells. The dual regulatory function of NR2E3 was not dependent upon the presence of NRL and/or CRX, but on the timing and level of its expression. Our studies reveal a critical role of NR2E3 in establishing functional specificity of NRL-expressing photoreceptor precursors during retinal neurogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".