Depletion of G9A attenuates imiquimod-induced psoriatic dermatitis via targeting EDAR-NF-κB signaling in keratinocyte
Bibliographic record
Abstract
Psoriasis is a common and recurrent inflammatory skin disease characterized by inflammatory cells infiltration of the dermis and excessive proliferation, reduced apoptosis, and abnormal keratosis of the epidermis. In this study, we found that G9A, an important methyltransferase that mainly mediates the mono-methylation (me1) and di-methylation (me2) of histone 3 lysine 9 (H3K9), is highly expressed in lesions of patients with psoriasis and imiquimod (IMQ)-induced psoriasis-like mouse model. Previous studies have shown that G9A is involved in the pathogenesis of various tumors by regulating apoptosis, proliferation, differentiation, and invasion. However, the role of G9A in skin inflammatory diseases such as psoriasis remains unclear. Our data so far suggest that topical administration of G9A inhibitor BIX01294 as well as keratinocyte-specific deletion of G9A greatly alleviated IMQ-induced psoriatic alterations in mice for the first time. Mechanistically, the loss function of G9A causes the downregulation of Ectodysplasin A receptor (EDAR), consequently inhibiting the activation of NF-κB pathway, resulting in impaired proliferation and increased apoptosis of keratinocytes, therefore ameliorating the psoriatic dermatitis induced by IMQ. In total, we show that inhibition of G9A improves psoriatic-like dermatitis mainly by regulating cell proliferation and apoptosis rather than inflammatory processes, and that this molecule may be considered as a potential therapeutic target for keratinocyte hyperproliferative diseases such as psoriasis.
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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".