Modeling Dominant and Recessive Forms of Retinitis Pigmentosa by Editing Three Rhodopsin-Encoding Genes in Xenopus Laevis Using Crispr/Cas9
Bibliographic record
Abstract
The utility of Xenopus laevis, a common research subject for developmental biology, retinal physiology, cell biology, and other investigations, has been limited by lack of a robust gene knockout or knock-down technology. Here we describe manipulation of the X. laevis genome using CRISPR/Cas9 to model the human disorder retinitis pigmentosa, and to introduce point mutations or exogenous DNA sequences. We introduced and characterized in-frame and out-of-frame insertions and deletions in three genes encoding rhodopsin by co-injection of Cas9 mRNA, eGFP mRNA, and single guide RNAs into fertilized eggs. Deletions were characterized by direct sequencing and cloning; phenotypes were assessed by assays of rod opsin in retinal extracts, and confocal microscopy of cryosectioned and immunolabeled contralateral eyes. We obtained germline transmission of editing to F1 offspring. In-frame deletions frequently caused dominant retinal degeneration associated with rhodopsin biosynthesis defects, while frameshift phenotypes were consistent with knockout. We inserted eGFP or point mutations into rhodopsin genes by co-injection of repair fragments with homology to the Cas9 target sites. Our techniques can produce high frequency gene editing in X. laevis, permitting analysis in the F0 generation, and advancing the utility of X. laevis as a subject for biological research and disease modeling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".