GLI3-dependent transcriptional repression of<i>Gli1, Gli2</i>and kidney patterning genes disrupts renal morphogenesis
Bibliographic record
Abstract
Truncating mutations in Gli3, an intracellular effector in the SHH-SMO-GLI signaling pathway, cause renal aplasia/dysplasia in humans and mice. Yet, the pathogenic mechanisms are undefined. Here, we report the effect of decreased SHH-SMO signaling on renal morphogenesis, the expression of SHH target genes and GLI binding to Shh target genes. Shh deficiency or cyclopamine-mediated SMO inhibition disrupted renal organogenesis, decreased expression of GLI1 and GLI2 proteins, but increased expression of GLI3 repressor relative to GLI3 activator. Shh deficiency decreased expression of kidney patterning genes (Pax2 and Sall1) and cell cycle regulators (cyclin D1 and MYCN). Elimination of Gli3 in Shh(-/-) mice rescued kidney malformation and restored expression of Pax2, Sall1, cyclin D1, MYCN, Gli1 and Gli2. To define mechanisms by which SHH-SMO signaling controls gene expression, we determined the binding of GLI proteins to 5' flanking regions containing GLI consensus binding sequences in Shh target genes using chromatin immunoprecipitation. In normal embryonic kidney tissue, GLI1 and/or GLI2 were bound to each target gene. By contrast, treatment of embryonic kidney explants with cyclopamine decreased GLI1 and/or GLI2 binding, and induced binding of GLI3. However, cyclopamine failed to decrease Gli1 and Gli2 expression and branching morphogenesis in Gli3-deficient embryonic kidney tissue. Together, these results demonstrate that SHH-SMO signaling controls renal morphogenesis via transcriptional control of Gli, renal patterning and cell cycle regulator genes in a manner that is opposed by GLI3.
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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) | 0.002 | 0.001 |
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".