Forced Expression of HLA-DM at the Surface of Dendritic Cells Increases Loading of Synthetic Peptides on MHC Class II Molecules and Modulates T Cell Responses
Bibliographic record
Abstract
Adoptive transfer of autologous dendritic cells (DCs) loaded with tumor-associated CD4 and CD8 T cell epitopes represents a promising avenue for the immunotherapy of cancer. In an effort to increase the loading of therapeutic synthetic peptides on MHC II molecules, we used a mutant of HLA-DM (DMY) devoid of its lysosomal sorting motif and that accumulates at the cell surface. Transfection of DMY into HLA-DR(+) cells resulted in increased loading of the exogenously supplied HA(307-318) peptide, as well as increased stimulation of HA-specific T cells. Also, on transduction in mouse and human DCs, DMY increased loading of HEL(48-61) and of the tumor Ag-derived gp100(174-190) peptides, respectively. Interestingly, expression of DMY at the surface of APCs favored Th1 differentiation over Th2. Finally, we found that DMY(-) and DMY(+) mouse APCs differentially stimulated T cell hybridomas sensitive to the fine conformation of peptide-MHC II complexes. Taken together, our results suggest that the overexpression of HLA-DMY at the plasma membrane of DCs may improve quantitatively, but also qualitatively, the presentation of CD4 T cell epitopes in cellular vaccine therapies for cancer.
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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".