Activation of β-catenin signaling programs embryonic epidermis to hair follicle fate
Bibliographic record
Abstract
beta-Catenin signaling is required for hair follicle development, but it is unknown whether its activation is sufficient to globally program embryonic epidermis to hair follicle fate. To address this, we mutated endogenous epithelial beta-catenin to a dominant-active form in vivo. Hair follicle placodes were expanded and induced prematurely in activated beta-catenin mutant embryos, but failed to invaginate or form multilayered structures. Eventually, the entire epidermis adopted hair follicle fate, broadly expressing hair shaft keratins in place of epidermal stratification proteins. Mutant embryonic skin was precociously innervated, and displayed prenatal pigmentation, a phenomenon never observed in wild-type controls. Thus, beta-catenin signaling programs the epidermis towards placode and hair shaft fate at the expense of epidermal differentiation, and activates signals directing pigmentation and innervation. In transcript profiling experiments, we identified elevated expression of Sp5, a direct beta-catenin target and transcriptional repressor. We show that Sp5 normally localizes to hair follicle placodes and can suppress epidermal differentiation gene expression. We identified the pigmentation regulators Foxn1, Adamts20 and Kitl, and the neural guidance genes Sema4c, Sema3c, Unc5b and Unc5c, as potential mediators of the effects of beta-catenin signaling on pigmentation and innervation. Our data provide evidence for a new paradigm in which, in addition to promoting hair follicle placode and hair shaft fate, beta-catenin signaling actively suppresses epidermal differentiation and directs pigmentation and nerve fiber growth. Controlled downregulation of beta-catenin signaling is required for normal placode patterning within embryonic ectoderm, hair follicle downgrowth, and adoption of the full range of follicular fates.
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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".