Reduction of elevated <i>Gli3</i> does not alter the progression of autosomal recessive polycystic kidney disease
Bibliographic record
Abstract
Polycystic kidney diseases (PKD) are genetic disorders which disrupt kidney architecture and function. Autosomal recessive PKD (ARPKD) is a rare form of PKD, caused by mutations in PKHD1, and clinically more severe than the more common autosomal dominant PKD (ADPKD). Prior studies have implicated Hedgehog (Hh) signaling in ADPKD, with increased levels of Hh components in experimental ADPKD and reduced cystogenesis following pharmacological Hh inhibition. In contrast, the role of the Hh pathway in ARPKD is poorly understood. We hypothesized that Hh pathway activity would be elevated during ARPKD pathogenesis, and its modulation may slow disease progression. We utilized Cpk mice which phenocopy ARPKD and generated a PKHD1-mutant spheroid model in human collecting ducts. Significantly elevated levels of the Hh transcriptional effector Gli3 were found in Cpk mice, a finding replicated in PKHD1-mutant spheroids. In Cpk mice, total GLI3 and GLI3 repressor protein levels were also increased. Reduction of increased Gli3 levels via heterozygous genetic deletion in Cpk mice did not affect cyst formation. Additionally, lowering GLI3 transcripts to wildtype levels did not influence PKHD1-mutant spheroid size. Collectively, these data suggest attenuation of elevated Gli3 does not modulate murine and human models of ARPKD.
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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".