Antigen Presenting B Cells Facilitate CD4 T Cell Cooperation Resulting in Enhanced Generation of Effector and Memory CD4 T Cells
Bibliographic record
Abstract
We show that the in vivo generation of cytokine-producing CD4 T cells specific for a given major histocompatibility class-II (MHCII)-binding peptide of hen egg lysozyme (HEL) is facilitated when mice are immunized with splenic antigen presenting cells (APC) pulsed with this HEL peptide and another peptide that binds a different MHCII molecule. This enhanced generation of peptide-specific effector CD4 T cells requires that the same splenic APC be pulsed with both peptides. Pulsed B cells, but not pulsed dendritic cells (DCs), can mediate CD4 T cell cooperation, which can be blocked by disrupting OX40-OX40L (CD134-CD252) interactions. In addition, the generation of HEL peptide-specific CD4 T cell memory is greater when mice are primed with B cells pulsed with the two peptides than with B cells pulsed with the HEL- peptide alone. Based on our findings, we suggest CD4 T cell cooperation is important for vaccine design, underlies the phenomenon of "epitope-spreading" seen in autoimmunity, and that the efficacy of B cell-depletion in the treatment of human cell-mediated autoimmune disease is due to the abrogation of the interactions between autoimmune CD4 T cells that facilitates their activation.
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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) | 0.001 | 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) | 0.002 | 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".