Keratinocyte <scp>EGF</scp> signalling dominates in atopic dermatitis lesions: A comparative <scp>RNAseq</scp> analysis
Bibliographic record
Abstract
Atopic dermatitis (AD) remains a highly heterogenous disorder with a multifactorial aetiology. Whilst keratinocytes are known to play a fundamental role in AD, their contribution to the overall immune landscape in moderate-to-severe AD is still poorly understood. In order to design new therapeutics, further investigation is needed into common disease pathways at the molecular level. We used publicly available whole-tissue RNAseq data (4 studies) and single-cell RNAseq keratinocyte data to identify genes/pathways that are involved in keratinocyte responses in AD and after dupilumab treatment. Transcripts present in both keratinocytes (single-cell) and whole-tissue, referred to as the keratinocyte-enriched lesional skin (KELS) genes, were analysed using functional/pathway analysis. Following statistical testing, 2049 genes (16.8%) were differentially expressed in KELS. Enrichment analyses predicted increases in not only type-1/type-2 immune signalling and chemoattraction, but also in EGF-dominated growth factor signalling. We identified complex crosstalk between keratinocytes and immune cells involving a dominant EGF family signature which converges on keratinocytes with potential immunomodulatory and chemotaxis-promoting consequences. Although keratinocytes express the IL4R, we observed no change in EGF signalling in KELS after three-month treatment with dupilumab, indicating that this pathway is not modulated by dupilumab immunotherapy. EGF family signalling is significantly dysregulated in AD lesions but is not associated with keratinocyte proliferation. EGF signalling pathways in AD require further study.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".