Autophagy is implicated in MHC class I antigen cross-presentation mediated by potexvirus VLP (106.10)
Bibliographic record
Abstract
Abstract The development of vaccination platforms capable of triggering an efficient cytotoxic-T-lymphocyte (CTL) response is a priority for eliciting a protective cellular immune response. The proposed technology is based on the use of a new adjuvant composed of a virus-like particle (VLP) with the chimeric coat protein of papaya mosaic virus (PapMV). This VLP can trigger a CTL response through MHC class I antigenic cross-presentation of inserted epitopes. We have wanted to establish the cellular pathway leading to cross-presentation by those VLP. First, we chemically inhibit different molecular path that can be important for cross-presentation. We observed that cathepsin S and autophagy are implicated in cross-presentation by PapMV VLP. Also, we localized the VLP by fluorescence microscopy in antigen presenting cells (APC). To confirm the role of autophagy, we observed its induction by LC3 western blot and microscopy. When the APC are incubated with VLP we observed an activation of autophagy by LC3 lipidation. Finally for more specific inhibition, we used shRNA against ATG5 and cathepsin S in APC. Both ATG5 and cathepsin S reduced the cross-presentation by VLP In conclusion, the cross-presentation mediated by PapMV VLP is dependant on autophagy and degradation by cathepsin S. A better understanding of this mechanism would lead to the development of an efficient vaccine platform against cancer and infectious diseases
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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".