Lipid Raft-dependent and -independent Signaling through HLA-DR Molecules
Bibliographic record
Abstract
Lipid rafts are plasma membrane microdomains that are highly enriched in signaling molecules and that act as signal transduction platforms for many immune receptors. The involvement of these microdomains in HLA-DR-induced signaling is less well defined. We examined the constitutive presence of HLA-DR molecules in lipid rafts, their possible recruitment into these microdomains, and the role of these microdomains in HLA-DR-induced responses. We detected significant amounts of HLA-DR molecules in the lipid rafts of EBV(+) and EBV(-) B cell lines, monocytic cell lines, transfected HeLa cells, tonsillar B cells, and human monocytes. Localization of HLA-DR in these microdomains was unaffected by the deletion of the cytoplasmic domain of both the alpha and beta chains. Ligation of HLA-DR with a bivalent, but not a monovalent, ligand resulted in rapid tyrosine phosphorylation of many substrates, especially Lyn, and activation of ERK1/2 MAP kinase. However, the treatment failed to induce further recruitment of HLA-DR molecules into lipid rafts. The HLA-DR-induced signaling events were accompanied by the induction of cell-cell adhesion that could be inhibited by PTK and Lyn but not ERK1/2 inhibitors. Disruption of lipid rafts by methyl-beta-cyclodextrin (MbetaCD) resulted in the loss of membrane raft association with HLA-DR molecules, inhibition of HLA-DR-mediated protein tyrosine phosphorylation and cell-cell adhesion. MbetaCD did not affect the activation of ERK1/2, which was absent from lipid rafts. These results indicate that although all the HLA-DR-induced events studied are dependent on HLA-DR dimerization, some require the presence of HLA-DR molecules in lipid rafts, whereas others do not.
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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) | 0.002 | 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".