A Key Role for Phosphoinositide 3‐Kinase in the Regulation of LPS‐ and TNF‐á‐induced CD44 Expression in Human Monocytic Cells
Bibliographic record
Abstract
Alteration in the levels of CD44 expression on monocytic cells by endotoxins and immunoregulatory cytokines may modulate the migratory potential of immune cells to inflammatory sites and the development of immune responses. LPS and the proinflammatory cytokine, TNF‐α act as important regulators of CD44 expression in monocytic cells. We have previously demonstrated that LPS‐ and TNF‐α‐induced CD44 expression is regulated by two independent signaling pathways in human monocytic cells (Mishra et al. JBC, 2005). LPS‐induced CD44 expression is regulated by the JNK‐activated Egr‐1 whereas CaMK‐II‐activated AP‐1 regulates TNF‐α‐induced CD44 expression. In this study, we show that PI 3‐kinase (PI3K) constitutes a key downstream component of both the signaling pathways involved in the regulation of LPS‐ and TNF‐α‐induced CD44 expression. Our results suggest that the JNK‐activated PI3K regulates LPS‐induced CD44 expression through the activation of Egr‐1 whereas TNF‐α induces CD44 expression by a distinct CaMK‐II‐activated PI3K through the activation of AP‐1. Taken together, our results suggest a critical involvement of PI3K in the regulation of LPS‐ and TNF‐α‐induced CD44 expression and hence may represent a potential therapeutic target for inhibiting CD44 expression and consequent CD44‐mediated cell migration, inflammation and autoimmune disorders. This work was supported by NSERC, Canada and OGSST.
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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".