Modeling Pkd1 gene-targeted strategies for correction of polycystic kidney disease
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) causes renal cysts and leads to end-stage renal disease in midlife due mainly to PKD1 gene mutations. Virtually no studies have explored gene therapeutic strategies for long-term effective treatment of PKD. Toward this aim, the severely cystic Pkd1 -null mouse model was targeted with a series of transgene transfers using genomic Pkd1 under its regulatory elements ( Pkd1 wt ), a kidney-targeted Pkd1 gene ( SB Pkd1 ), or Pkd1 Minigene . The introduced Pkd1 wt gene constructs with ∼8-fold overexpression display similar endogenous cellular profiles and full complementation of Pkd1 −/− phenotype and establish the referral Pkd1 genomic length for proper regulation. SB Pkd1 transgene transfer expressing 0.6- or 7-fold Pkd1 endogenous levels is sufficient to correct glomerular and proximal tubular cysts and to markedly postpone cysts in other tubular segments as well, showing that the small SB elements appreciably overlap with Pkd1 promoter/5′ UTR regulation. Renal-targeted Pkd1 Minigene at high copy numbers conveys an expression level similar to that of the endogenous Pkd1 gene, with widespread and homogeneous weak Pkd1 cellular signal, partially rescuing all cystic tubular segments. These transgene transfers determine that Pkd1 intragenic sequences regulate not only expression levels but also spatiotemporal patterns. Importantly, our study demonstrates that Pkd1 re-expression from hybrid therapeutic constructs can ameliorate, with considerably extended lifespan, or eliminate PKD.
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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 | 0.001 | 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".