Zinc‐finger‐based transcriptional repression of rhodopsin in a model of dominant retinitis pigmentosa
Bibliographic record
Abstract
Despite the recent success of gene-based complementation approaches for genetic recessive traits, the development of therapeutic strategies for gain-of-function mutations poses great challenges. General therapeutic principles to correct these genetic defects mostly rely on post-transcriptional gene regulation (RNA silencing). Engineered zinc-finger (ZF) protein-based repression of transcription may represent a novel approach for treating gain-of-function mutations, although proof-of-concept of this use is still lacking. Here, we generated a series of transcriptional repressors to silence human rhodopsin (hRHO), the gene most abundantly expressed in retinal photoreceptors. The strategy was designed to suppress both the mutated and the wild-type hRHO allele in a mutational-independent fashion, to overcome mutational heterogeneity of autosomal dominant retinitis pigmentosa due to hRHO mutations. Here we demonstrate that ZF proteins promote a robust transcriptional repression of hRHO in a transgenic mouse model of autosomal dominant retinitis pigmentosa. Furthermore, we show that specifically decreasing the mutated human RHO transcript in conjunction with unaltered expression of the endogenous murine Rho gene results in amelioration of disease progression, as demonstrated by significant improvements in retinal morphology and function. This zinc-finger-based mutation-independent approach paves the way towards a 'repression-replacement' strategy, which is expected to facilitate widespread applications in the development of novel therapeutics for a variety of disorders that are due to gain-of-function mutations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".