Role of Sulfation in CD44-Mediated Hyaluronan Binding Induced by Inflammatory Mediators in Human CD14+ Peripheral Blood Monocytes
Bibliographic record
Abstract
Activation of T cells by Ag or stimulation of monocytes with inflammatory cytokines induces CD44 to bind to hyaluronan (HA), an adhesion event implicated in leukocyte-leukocyte, leukocyte-endothelial cell, and leukocyte-stromal cell interactions. We have previously shown that TNF-alpha induces CD44 sulfation in a leukemic cell line, which correlated with the induction of HA binding and CD44-mediated adhesion. In this study, we establish that TNF-alpha and IFN-gamma induce HA binding and the sulfation of CD44 in CD14(+) PBMC, whereas no induced HA binding or CD44 sulfation was observed in CD14(-) PBMC stimulated with TNF-alpha. Treatment of cells with NaClO(3), an inhibitor of sulfation, prevented HA binding in a significant percentage of CD14(+) PBMC induced by TNF-alpha, LPS, IL-1beta, or IFN-gamma. Furthermore, stimulation with TNF-alpha or IFN-gamma in the presence of NaClO(3) reduced the ability of isolated CD44H to bind HA, demonstrating a direct effect of CD44H sulfation on HA binding. In contrast, the transient induction of HA binding in T cells by PHA was not affected by NaClO(3), suggesting that activated T cells do not use sulfation as a mechanism to regulate HA binding. Overall, these results demonstrate that inducible sulfation of CD44H is one mechanism used by CD14(+) peripheral blood monocytes to induce HA binding in response to inflammatory agents such as TNF-alpha and IFN-gamma.
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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) | 0.001 | 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".