Inducible deletion of epidermal <i>Dicer</i> and <i>Drosha</i> reveals multiple functions for miRNAs in postnatal skin
Bibliographic record
Abstract
MicroRNAs (miRNAs) regulate the expression of many mammalian genes and play key roles in embryonic hair follicle development; however, little is known of their functions in postnatal hair growth. We compared the effects of deleting the essential miRNA biogenesis enzymes Drosha and Dicer in mouse skin epithelial cells at successive postnatal time points. Deletion of either Drosha or Dicer during an established growth phase (anagen) caused failure of hair follicles to enter a normal catagen regression phase, eventual follicular degradation and stem cell loss. Deletion of Drosha or Dicer in resting phase follicles did not affect follicular structure or epithelial stem cell maintenance, and stimulation of anagen by hair plucking caused follicular proliferation and formation of a primitive transient amplifying matrix population. However, mutant matrix cells exhibited apoptosis and DNA damage and hair follicles rapidly degraded. Hair follicle defects at early time points post-deletion occurred in the absence of inflammation, but a dermal inflammatory response and hyperproliferation of interfollicular epidermis accompanied subsequent hair follicle degradation. These data reveal multiple functions for Drosha and Dicer in suppressing DNA damage in rapidly proliferating follicular matrix cells, facilitating catagen and maintaining follicular structures and their associated stem cells. Although Drosha and Dicer each possess independent non-miRNA-related functions, the similarity in phenotypes of the inducible epidermal Drosha and Dicer mutants indicates that these defects result primarily from failure of miRNA processing. Consistent with this, Dicer deletion resulted in the upregulation of multiple direct targets of the highly expressed epithelial miRNA miR-205.
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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 | 0.001 |
| 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".