Using the TAP Component of the Antigen-Processing Machinery as a Molecular Adjuvant
Bibliographic record
Abstract
We hypothesize that over-expression of transporters associated with antigen processing (TAP1 and TAP2), components of the major histocompatibility complex (MHC) class I antigen-processing pathway, enhances antigen-specific cytotoxic activity in response to viral infection. An expression system using recombinant vaccinia virus (VV) was used to over-express human TAP1 and TAP2 (VV-hTAP1,2) in normal mice. Mice coinfected with either vesicular stomatitis virus plus VV-hTAP1,2 or Sendai virus plus VV-hTAP1,2 increased cytotoxic lymphocyte (CTL) activity by at least 4-fold when compared to coinfections with a control vector, VV encoding the plasmid PJS-5. Coinfections with VV-hTAP1,2 increased virus-specific CTL precursors compared to control infections without VV-hTAP1,2. In an animal model of lethal viral challenge after vaccination, VV-hTAP1,2 provided protection against a lethal challenge of VV at doses 100-fold lower than control vector alone. Mechanistically, the total MHC class I antigen surface expression and the cross-presentation mechanism in spleen-derived dendritic cells was augmented by over-expression of TAP. Furthermore, VV-hTAP1,2 increases splenic TAP transport activity and endogenous antigen processing, thus rendering infected targets more susceptible to CTL recognition and subsequent killing. This is the first demonstration that over-expression of a component of the antigen-processing machinery increases endogenous antigen presentation and dendritic cell cross-presentation of exogenous antigens and may provide a novel and general approach for increasing immune responses against pathogens at low doses of vaccine inocula.
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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 | 0.001 |
| 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".