Ras, Protein Kinase Cζ, and IκB Kinases 1 and 2 Are Downstream Effectors of CD44 During the Activation of NF-κB by Hyaluronic Acid Fragments in T-24 Carcinoma Cells
Bibliographic record
Abstract
We have investigated the ability of hyaluronic acid (HA) fragments to activate the transcription factor NF-kappa B. HA fragments activated NF-kappa B in the cell lines T-24, HeLa, MCF7, and J774. Further studies in T-24 cells demonstrated that HA fragments also induced I kappa B alpha phosphorylation and degradation, kappa B-linked reporter gene expression, and ICAM-1 promoter activity in an NF-kappa B-dependent manner. The effect of HA was size dependent as neither disaccharide nor native HA were active. CD44, the principal cellular receptor for HA, was critical for the response because the anti-CD44 Ab IM7.8.1 blocked the effect on NF-kappa B. HA fragments activated the I kappa B kinase complex, and the effect on a kappa B-linked reporter gene was blocked in T-24 cells expressing dominant negative I kappa B kinases 1 or 2. Activation of protein kinase C (PKC) was required because calphostin C inhibited NF-kappa B activation and I kappa B alpha phosphorylation. In particular, PKC zeta was required because transfection of cells with dominant negative PKC zeta blocked the effect of HA fragments on kappa B-linked gene expression and HA fragments increased PKC zeta activity. Furthermore, damnacanthal and manumycin A, two mechanistically distinct inhibitors of Ras, blocked NF-kappa B activation. Transfection of T-24 cells with dominant negative Ras (RasN17) blocked HA fragment-induced kappa B-linked reporter gene expression, and HA fragments activated Ras activity within 5 min. Taken together, these studies establish a novel signal transduction cascade emanating from CD44 to Ras, PKC zeta, and I kappa B kinase 1 and 2.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".