MHC class I antigen cross-presentation mediated by PapMV nanoparticles in human antigen-presenting cells is dependent on autophagy
Bibliographic record
Abstract
Nanoparticles made of the coat protein of papaya mosaic virus (PapMV) and a single-strand RNA were previously shown to be an efficient antigen presentation system for the trigger of cellular immunity. Engineering of PapMV nano with a cytotoxic T lymphocyte epitope was previously shown activating specific T lymphocytes through a proteasome-independent major histocompatibility complex class I (MHC-I) cross-presentation. In this study, we provide new insights into the mechanism of the MHC-I cross-presentation mediated by PapMV nanoparticles. We demonstrate that PapMV nanoparticles do not require the transporter associated with antigen presentation (TAP), but rather depend on lysosome acidification and cathepsin S protease activity for presentation of the T cell epitope. We have also linked the induction of autophagy with this vacuolar MHC-I cross-presentation process. Interestingly, autophagy is induced in antigen-presenting cells after PapMV nanoparticles exposure and inhibition of autophagy reduce MHC-I cross-presentation. This study demonstrates that autophagy is associated with TAP- and proteasome-independent MHC-I cross-presentation. A deeper understanding of the autophagy-dependent MHC-I cross-presentation will be useful in designing vaccination platforms that aim to trigger an efficient cytotoxic T lymphocyte response.
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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".