Deletion of <i>S100a8</i> and <i>S100a9</i> Enhances Skin Hyperplasia and Promotes the Th17 Response in Imiquimod-Induced Psoriasis
Bibliographic record
Abstract
Abstract High concentrations of the damage-associated molecular patterns S100A8 and S100A9 are found in skin and serum from patients suffering from psoriasis, an IL-17–related disease. Notably, although the expression of these proteins correlates with psoriatic disease severity, the exact function of S100A8 and S100A9 in psoriasis pathogenesis remains unclear. In this study, we investigated the role of S100A8 and S100A9 in psoriasis-associated skin hyperplasia and immune responses using S100a8−/− and S100a9−/− mice in an imiquimod-induced model of psoriasis. We found that S100a8−/− and S100a9−/− psoriatic mice exhibit worsened clinical symptoms relative to wild-type mice and increased expression of S100A9 and S100A8 proteins in keratinocytes, respectively. In addition, the loss of S100A8 enhances proliferation of keratinocytes and disrupts keratinocyte differentiation. We further detected elevated production of IL-17A and -F from CD4+ T cells in the absence of S100A8 and S100A9, as well as increased infiltration of neutrophils in the skin. In addition, treatment with anti–IL-17A and -F was found to reduce psoriasis symptoms and skin hyperplasia in S100a8−/− and S100a9−/− mice. These data suggest that S100A8 and S100A9 regulate psoriasis by inhibiting production of IL-17A and -F, thereby, to our knowledge, providing new insights into their biological functions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".