Proteasome-Independent Major Histocompatibility Complex Class I Cross-Presentation Mediated by Papaya Mosaic Virus-Like Particles Leads to Expansion of Specific Human T Cells
Bibliographic record
Abstract
The development of versatile vaccine platforms is a priority that is recognized by health authorities worldwide; such platforms should induce both arms of the immune system, the humoral and cytotoxic-T-lymphocyte responses. In this study, we have established that a vaccine platform based on the coat protein of papaya mosaic virus (PapMV CP), previously shown to induce a humoral response, can induce major histocompatibility complex (MHC) class I cross-presentation of HLA-A*0201 epitopes from gp100, a melanoma antigen, and from influenza virus M1 matrix protein. PapMV proteins were able to assemble into stable virus-like particles (VLPs) in a crystalline and repetitive structure. When we pulsed HLA-A*0201+ antigen-presenting cells (APCs) with the recombinant PapMV FLU or gp100, we noted that antigen-specific CD8+ T cells were highly reactive to these APCs, demonstrating that the epitope from the VLPs were processed and loaded on the MHC class I complex. APCs were preincubated with two different proteasome inhibitors, which did not affect the efficiency of peptide presentation on MHC class I. Classical presentation from an endogenous antigen was abolished in the same conditions. Clearly, antigen presentation mediated by the PapMV system was proteasome independent. Finally, PapMV-pulsed APCs had the capacity to expand highly avid antigen-specific T cells against the influenza virus M1 HLA-A*0201 epitope when cocultured with autologous peripheral blood mononuclear cells. This study demonstrates the potential of PapMV for MHC class I cross-presentation and for the expansion of human antigen-specific T cells. It makes VLPs from PapMV CP a very attractive platform to trigger cellular responses for vaccine development against chronic infectious diseases and cancers.
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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".