Translational readthrough therapy for ADPKD induces polycystin1 expression and partially rescues functional deficits in PKD1 mutant cells
Bibliographic record
Abstract
Autosomal-Dominant Polycystic Kidney Disease, ADPKD, is the most common genetic kidney disease affecting 1:1000 people worldwide. It is caused by mutations in the PKD1 (~ 80%) or PKD2 gene (~ 15%). Although the germline mutation is inherited in dominant fashion, disabling the second allele is required for emergence of clonal cysts. Presently, no cure exists for ADPKD. In approximately 30% of patients, the heritable ADPKD mutation involves a single nucleotide substitution that converts the normal mRNA triplet encoding an amino acid into a Premature Termination Codon (PTC). The translation machinery poses at the PTC and detaches from the mutant mRNA; the unstable transcript and protein are degraded. Certain aminoglycosides bind to the mammalian ribosome and relax translational fidelity, permitting continued translation and production of a full-length protein. In this study, we tested the ability of aminoglycosides to induce readthrough of the PTC codons in the human PKD1 gene and ascertained the effect of these drugs on pathologic features of PKD1 mutant cells. We report that aminoglycosides induce 8-25% expression of full-length Polycystin1 (PKD1 gene product) and significantly improve aberrant cell adhesion and cell signaling. Based on our observations, we propose that aminoglycoside readthrough drugs show potential as therapeutic agents for ADPKD.
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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 | 0.001 |
| 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".