Epidermal ADAM17 provides barrier immunity and prevents myeloproliferative disease by regulating Notch-AP1 interaction (166.7)
Bibliographic record
Abstract
Abstract Epithelial cells are the major component of mucosal tissues such as the skin, gut and lungs. They are critical barriers against environmental stress factors which induce significant immunopathology. Keratinocytes in the skin may act as key decision-makers of immune cell function by directing local adaptive immune responses and hematopoietic development, however mechanisms driving epithelial:immune crosstalk are incompletely understood. The cell surface sheddase A Disintegrin and Metalloproteinase 17 (ADAM17) cleaves numerous proteins with key roles in development, stress response and inflammation. Here we show that loss of epidermal Adam17 leads to severe atopic dermatitis and myeloproliferative disease. ADAM17 deficiency in keratinocytes enhanced AP1 signaling, increasing the production of epithelial cytokines (e.g. TSLP, IL-33) to drive Th2-polarized lymphocyte activation. We also observed the release of myeloid growth factors (e.g. G-CSF), causal to myeloproliferative disease in these mice. Next, we identified that ADAM17 promotes ligand-independent Notch activation in keratinocytes, revealing a novel mechanism of Notch-AP1 interaction that abrogates stress signaling. Notably, ectopic Notch activation in the epidermis of ADAM17 deficient mice rescued local inflammation and myeloproliferation. Thus Notch activation by ADAM17 in keratinocytes antagonizes AP1-induced inflammation, thereby establishing a mechanism of epithelial:immune crosstalk.
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) | 0.003 | 0.001 |
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".