Injury-induced interleukin-1 alpha promotes Lgr5 hair follicle stem cells de novo regeneration and proliferation via regulating regenerative microenvironment in mice
Bibliographic record
Abstract
BACKGROUND: The hair follicles (HFs) are barely regenerated after loss in injuries in mammals as well as in human beings. Recent studies have shown that the regenerative ability of HFs is age-related; however, the relationship between this phenomenon and the stem cell niche remains unclear. This study aimed to find a key secretory protein that promotes the HFs regeneration in the regenerative microenvironment. METHODS: To explore why age affects HFs de novo regeneration, we established an age-dependent HFs regeneration model in leucine-rich repeat G protein-coupled receptor 5 (Lgr5) + /mTmG mice. Proteins in tissue fluids were analyzed by high-throughput sequencing. The role and mechanism of candidate proteins in HFs de novo regeneration and hair follicle stem cells (HFSCs) activation were investigated through in vivo experiments. The effects of candidate proteins on skin cell populations were investigated by cellular experiments. RESULTS: Mice under 3-week-old (3W) could regenerate HFs and Lgr5 HFSCs, which were highly correlated with the immune cells, cytokines, IL-17 signaling pathway, and IL-1α level in the regeneration microenvironment. Additionally, IL-1α injection induced de novo regeneration of HFs and Lgr5 HFSCs in 3W mouse model with a 5 mm wound, as well as promoted activation and proliferation of Lgr5 HFSCs in 7-week-old (7W) mice without wound. Dexamethasone and TEMPOL inhibited the effects of IL-1α. Moreover, IL-1α increased skin thickness and promoted the proliferation of human epidermal keratinocyte line (HaCaT) and skin-derived precursors (SKPs) in vivo and in vitro, respectively. CONCLUSIONS: In conclusion, injury-induced IL-1α promotes HFs regeneration by modulating inflammatory cells and oxidative stress-induced Lgr5 HFSCs regeneration as well as promoting skin cell populations proliferation. This study uncovers the underlying molecular mechanisms enabling HFs de novo regeneration in an age-dependent model.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".