The polygenic architecture of hidradenitis suppurativa reveals signaling mechanisms that implicate epithelial remodeling
Bibliographic record
Abstract
Abstract We sought to identify clinically relevant regulators of hair follicle inflammation by conducting a human genetic study of hidradenitis suppurativa (HS), a prevalent, understudied, inflammatory disease with limited effective treatments. We performed a GWAS with 6,300 cases and identified 12 independent risk loci. Epigenetic and transcriptomic analyses of HS risk variants defined cell-specific gene regulatory programs. We experimentally validated a coherent gene module defined by upregulated SOX9 , CXCR4 , and CD74 co-expression that maps to aberrant epithelial structures in the skin. Pharmacological inhibition of CXCR4 implicates CD74 mediated regulation of PI3K/AKT and NF-κB signaling to calibrate inflammation, proliferation and apoptosis in keratinocytes. We next used genome-wide methods to interrogate shared polygenic architecture and identified new clinically and mechanistically relevant disease associations, including another condition that involves aberrant hair follicle remodeling, male pattern hair loss. Our results point towards CXCR4-CD74 signaling in HS and hair follicle homeostasis and suggest CXCR4 blockade as a new therapeutic strategy in HS.
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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 | 0.000 | 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".