Apolipoprotein-mediated lipid antigen presentation in B cells provides a pathway for innate help by NKT cells
Bibliographic record
Abstract
Natural killer T (NKT) cells are innate-like lymphocytes that recognize lipid antigens and have been shown to enhance B-cell activation and antibody production. B cells typically recruit T-cell help by presenting internalized antigens recognized by their surface antigen receptor. Here, we demonstrate a highly efficient means whereby human B cells present lipid antigens to NKT cells, capturing the antigen using apolipoprotein E (apoE) and the low-density lipoprotein receptor (LDL-R). ApoE dramatically enhances B-cell presentation of alpha-galactosylceramide (alphaGalCer), an exogenous CD1d presented antigen, inducing activation of NKT cells and the subsequent activation of B cells. B cells express the LDL-R on activation, and the activation of NKT cells by B cells is completely LDL-R dependent, as shown by blocking experiments and the complete lack of presentation when using apoE2, an isoform of apoE incapable of LDL-R binding. The dependence on apoE and the LDL-R is much more pronounced in B cells than we had previously seen in dendritic cells, which can apparently use alternate pathways of lipid antigen uptake. Thus, B cells use an apolipoprotein-mediated pathway of lipid antigen presentation, which constitutes a form of innate help for B cells by NKT cells.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".