STAT6 is a novel regulator of CD44 expression in human B cells (97.4)
Bibliographic record
Abstract
Abstract Interactions of CD44, an adhesion molecule, with its ligand, hyaluronan (HA) play a crucial role in cell migration, inflammation and immune response. The regulation of CD44 expression particularly in human B cells is not well understood. We have previously demonstrated that CD44 expression in human monocytic cells is regulated by Egr-1 and AP-1 transcription factors (Mishra et al. 2005, JBC). IL-4, a pleotropic cytokine is known to regulate CD44-HA interactions, cell migration, and differentiation of human B cells. To understand the regulation of IL-4-induced CD44 expression in human B cells, we used an EBV-transformed Burkitt’s lymphoma cell line, BL30/B95-8 as a model system. Our results suggested that IL-4 did not induce the expression of Egr-1 and AP-1 as determined by Northern and Western blot analysis and gel shift assays. To elucidate the transcription factors involved, we demonstrated by promoter analysis and gel shift assays that STAT-6 plays a critical role in IL-4-induced CD44 regulation in human B cells. We also investigated the upstream signaling events and demonstrated that IL-4-induced STAT-6 is activated by two distinct signaling pathways namely Jak-1/3 and ERK-MAPK and is independent of the IRS2/PI3 kinase pathway. Taken together, our results suggest a novel pathway of CD44 regulation involving the activation of STAT-6 through the upstream Jak-1/3 and ERK-MAPK in IL-4-stimulated human B cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.005 | 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".