Cholesterol Pathway Gene Variants and Reduced Keratinocyte Cholesterol Support a Final Common Druggable Pathway in Hyperproliferative Inflammatory Skin Diseases
Bibliographic record
Abstract
Hyperproliferative inflammatory skin disease (HISD) is frequently seen in rare monogenic diseases of cholesterol metabolism and responds to topical cholesterol/statin. We hypothesized that aberrant cholesterol metabolism within keratinocytes could be important in HISD more generally, driven by either immunological or lipid pathway genetic variation. Whereas other epidermal lipids have been well-characterized in HISDs, cholesterol and its metabolites have not. In this study, using comprehensive 2-dimensional gas chromatography 3-dimensional mass spectrometry, we found that primary keratinocytes from diverse monogenic HISDs (inflammatory linear verrucous epidermal nevi, n = 14; CHILD [congenital hemidysplasia with ichthyosiform erythroderma and limb defects] syndrome, n = 2) and from plaque psoriasis (n = 2) demonstrate significantly reduced mean cholesterol across all patient groups compared with those across the controls. This striking abnormality appears causally implicated because treatment in vitro with cholesterol and statin rescued the cellular hyperproliferation. Using SNPsea and burden analysis of large international psoriasis cohorts, we went on to show that GWAS hits were significantly enriched in proximity to genes encoding lipid metabolic pathways and that rare variants in lipid metabolic pathway genes were significantly enriched in patients with psoriasis. These data identify a final common pathway of aberrant keratinocyte cholesterol metabolism in HISD, which should be drugged topically to avoid first-pass metabolism. In parallel, we implicate genetic variation in lipid pathway genes in psoriasis susceptibility, potentially explaining the comorbidity of abnormal serum lipid profile and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".