Kif7 regulates Gli2 through Sufu-dependent and -independent functions during skin development and tumorigenesis
Bibliographic record
Abstract
Abnormal activation of Hedgehog (Hh) signaling leads to basal cell carcinoma (BCC) of the skin, the most common human cancer. Gli2, the major transcriptional activator of Hh signaling, is essential for hair follicle development and its overexpression in epidermis induces BCC formation and maintains tumor growth. Despite its importance in skin development and tumorigenesis, little is known about the molecular regulation of Gli2. Sufu and Kif7 are two evolutionarily conserved regulators of Gli transcription factors. Here, we show that Sufu and Kif7 regulate Gli2 through distinct mechanisms in keratinocytes. Sufu restricts the activity of Gli2 through cytoplasmic sequestration. Kif7 possesses Sufu-dependent and -independent regulatory functions in Hh signaling: while it promotes Hh pathway activity through the dissociation of Sufu-Gli2 complex, it also contributes to the repression of Hh target genes in the absence of Sufu. Deletion of both Sufu and Kif7 in embryonic skin leads to complete loss of follicular fate. Importantly, although inactivation of Sufu or Kif7 alone in adult epidermis cannot promote BCC formation, their simultaneous deletion induces BCC. These studies establish Sufu and Kif7 as crucial components in the regulation of Gli2 localization and activity, and illustrate their overlapping functions in skin development and tumor suppression.
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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 | 0.000 |
| 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".