Pygo2 regulates β-catenin–induced activation of hair follicle stem/progenitor cells and skin hyperplasia
Bibliographic record
Abstract
Understanding the epigenetic mechanisms that control the activation of adult stem cells holds the promise of tissue and organ regeneration. Hair follicle stem cells have emerged as a prime model to study stem cell activation. Wnt/β-catenin signaling controls multiple aspects of skin epithelial regeneration, with its excessive activity promoting the hyperactivation of hair follicle stem/progenitor cells and tumorigenesis. The contribution of chromatin factors in regulating Wnt/β-catenin pathway function in these processes is unknown. Here, we show that chromatin effector Pygopus homolog 2 (Pygo2) produced by the epithelial cells facilitates depilation-induced hair regeneration, as well as β-catenin-induced activation of hair follicle stem/early progenitor cells and trichofolliculoma-like skin hyperplasia. Pygo2 maximizes the expression of Wnt/β-catenin targets, but is dispensable for β-catenin-mediated expansion of LIM/homeobox protein Lhx2(+) cells, in the stem/early progenitor cell compartment of the hair follicle. Moreover, β-catenin and Pygo2 converge to induce the accumulation and acetylation of tumor suppressor protein p53 upon the cell cycle entry of hair follicle early progenitor cells and in cultured keratinocytes. These findings identify Pygo2 as an important regulator of Wnt/β-catenin function in skin epithelia and p53 activation as a prominent downstream event of β-catenin/Pygo2 action in stem cell activation.
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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) | 0.002 | 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".