Coal tar induces AHR-dependent skin barrier repair in atopic dermatitis
Bibliographic record
Abstract
Topical application of coal tar is one of the oldest therapies for atopic dermatitis (AD), a T helper 2 (Th2) lymphocyte-mediated skin disease associated with loss-of-function mutations in the skin barrier gene, filaggrin (FLG). Despite its longstanding clinical use and efficacy, the molecular mechanism of coal tar therapy is unknown. Using organotypic skin models with primary keratinocytes from AD patients and controls, we found that coal tar activated the aryl hydrocarbon receptor (AHR), resulting in induction of epidermal differentiation. AHR knockdown by siRNA completely abrogated this effect. Coal tar restored filaggrin expression in FLG-haploinsufficient keratinocytes to wild-type levels, and counteracted Th2 cytokine-mediated downregulation of skin barrier proteins. In AD patients, coal tar completely restored expression of major skin barrier proteins, including filaggrin. Using organotypic skin models stimulated with Th2 cytokines IL-4 and IL-13, we found coal tar to diminish spongiosis, apoptosis, and CCL26 expression, all AD hallmarks. Coal tar interfered with Th2 cytokine signaling via dephosphorylation of STAT6, most likely due to AHR-regulated activation of the NRF2 antioxidative stress pathway. The therapeutic effect of AHR activation herein described opens a new avenue to reconsider AHR as a pharmacological target and could lead to the development of mechanism-based drugs for AD.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".