Loss of a quiescent niche but not follicle stem cells in the absence of bone morphogenetic protein signaling
Bibliographic record
Abstract
During the hair cycle, follicle stem cells (SCs) residing in a specialized niche called the "bulge" undergo bouts of quiescence and activation to cyclically regenerate new hairs. Developmental studies have long implicated the canonical bone morphogenetic protein (BMP) pathway in hair follicle (HF) determination and differentiation, but how BMP signaling functions in the hair follicle SC niche remains unknown. Here, we use loss and gain of function studies to manipulate BMP signaling in the SC niche. We show that when the Bmpr1a gene is conditionally ablated, otherwise quiescent SCs are activated to proliferate, causing an expansion of the niche and loss of slow-cycling cells. Surprisingly, follicle SCs are not lost, however, but rather, they generate long-lived, tumor-like branches that express Sox4, Lhx2, and Sonic Hedgehog but fail to terminally differentiate to make hair. A key component of BMPR1A-deficient SCs is their elevated levels of both Lef1 and beta-catenin, which form a bipartite transcription complex required for initiation of the hair cycle. Although beta-catenin can be stabilized by Wnt signaling, we show that BMPR1A deficiency enhances beta-catenin stabilization in the niche through a pathway involving PTEN inhibition and PI3K/AKT activation. Conversely, sustained BMP signaling in the SC niche blocks activation and promotes premature hair follicle differentiation. Together, these studies reveal the importance of balancing BMP signaling in the SC niche.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".