Induction of <scp>GITRL</scp> expression in human keratinocytes by <scp>T</scp>h2 cytokines and <scp>TNF</scp>‐α: implications for atopic dermatitis
Bibliographic record
Abstract
BACKGROUND: Glucocorticoid-induced TNF receptor-related protein ligand (GITRL), a ligand for the T cell co-stimulatory molecule GITR, is expressed by keratinocytes and involved in chemokine production. The expression of GITRL in skin inflammation remains unknown. OBJECTIVES: This study investigated cytokine regulation of keratinocyte GITRL expression. METHODS: Glucocorticoid-induced TNF receptor expression was evaluated in cytokine-treated human epidermal keratinocytes (HEK)s, murine PAM 212 cell line, murine and human skin explants by real time PCR, flow cytometry and immunostaining. Functional responses to GITR fusion protein were examined by real time PCR and ELISA. GITRL expression in AD and psoriasis was studied by immunohistochemistry. RESULTS: Skin biopsies from STAT6VT transgenic mice, which develop spontaneous atopic skin inflammation, were found by immunofluoresence, to have increased keratinocyte GITRL expression. Exposure to Th2 cytokines augmented GITRL mRNA expression in the murine PAM 212 keratinocytic cell line and murine skin explants. In contrast, GITRL mRNA and protein expression was only increased in HEKs and human skin explants in the presence of the combination of TNF-α and Th2 cytokines. A synergistic effect of Th2 cytokines and GITR fusion protein on production of CCL17, the Th2 chemokine, by murine keratinocytes was demonstrated. Immunohistochemical staining showed that acute AD lesions have increased expression of GITRL compared with normal skin, chronic AD lesions and psoriatic plaques. CONCLUSIONS AND CLINICAL RELEVANCE: Our studies demonstrate that GITRL expression is augmented by Th2 cytokines and TNF-α in keratinocytes. Increased GITRL expression in acute AD skin lesions is shown. This observation suggests a link between cytokine-regulated keratinocyte GITRL expression and its role in inflammatory responses in AD.
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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 | 0.000 |
| 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 | 0.001 |
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".