Temporal and Tissue Specific Regulation of RP-Associated Splicing Factor Genes PRPF3, PRPF31 and PRPC8—Implications in the Pathogenesis of RP
Bibliographic record
Abstract
BACKGROUND: Genetic mutations in several ubiquitously expressed RNA splicing genes such as PRPF3, PRP31 and PRPC8, have been found to cause retina-specific diseases in humans. To understand this intriguing phenomenon, most studies have been focused on testing two major hypotheses. One hypothesis assumes that these mutations interrupt retina-specific interactions that are important for RNA splicing, implying that there are specific components in the retina interacting with these splicing factors. The second hypothesis suggests that these mutations have only a mild effect on the protein function and thus affect only the metabolically highly active cells such as retinal photoreceptors. METHODOLOGY/PRINCIPAL FINDINGS: We examined the second hypothesis using the PRPF3 gene as an example. We analyzed the spatial and temporal expression of the PRPF3 gene in mice and found that it is highly expressed in retinal cells relative to other tissues and its expression is developmentally regulated. In addition, we also found that PRP31 and PRPC8 as well as snRNAs are highly expressed in retinal cells. CONCLUSIONS/SIGNIFICANCE: Our data suggest that the retina requires a relatively high level of RNA splicing activity for optimal tissue-specific physiological function. Because the RP18 mutation has neither a debilitating nor acute effect on protein function, we suggest that retinal degeneration is the accumulative effect of decades of suboptimal RNA splicing due to the mildly impaired protein.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".