Differential Involvement of Calmodulin-dependent Protein Kinase II-activated AP-1 and c-Jun N-terminal Kinase-activated EGR-1 Signaling Pathways in Tumor Necrosis Factor-α and Lipopolysaccharide-induced CD44 Expression in Human Monocytic Cells
Bibliographic record
Abstract
CD44 plays a crucial role in cell migration, inflammation, and immune responses. Alteration in the levels of CD44 expression on monocytic cells by endotoxins and immunoregulatory cytokines may modulate the migration of immune cells to inflammatory sites and the development of immune responses. Lipopolysaccharide (LPS) and the proinflammatory cytokine, tumor necrosis factor-alpha (TNF-alpha), act as important regulators of CD44 expression in human monocytic cells. We previously demonstrated that the c-Jun N-terminal kinase (JNK), a mitogen-activated protein kinase (MAPK), differentially regulated LPS- but not TNF-alpha-induced CD44 expression in monocytic cells. In this study, our results suggest that the calcium signaling pathway, in particular calmodulin (CaM) and CaM-dependent protein kinase II (CaMK-II), is involved in TNF-alpha- but not LPS-induced CD44 expression. CD44 promoter analysis suggested the participation of distinct transcription factors AP-1 and Egr-1 in TNF-alpha- and LPS-induced CD44 expression, respectively. Furthermore, TNF-alpha-induced CD44 expression was regulated by AP-1 through the activation of the CaMK-II pathway, whereas LPS-induced CD44 transcription was regulated specifically by Egr-1 through JNK activation. Overall, the results suggest the involvement of two distinct and independent signaling pathways involved in the regulation of CD44 transcription that may represent potential targets for anti-inflammatory agents capable of inhibiting CD44-mediated cell migration.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".