Targeting 4-1BB (CD137) to enhance CD8 T cell responses with poxviruses and viral antigens
Bibliographic record
Abstract
Attenuated vaccinia virus (VACV) vectors are considered prime vaccine candidates for use in immunotherapy of infectious disease. In spite of this, recent data show that the level of attenuation may hamper the efficient generation of protective CD8 T cells. This suggests that additional adjuvant-like activities may need to be combined with attenuated VACV for optimal vaccination. Stimulatory reagents to the TNFR family molecule 4-1BB (CD137) may represent such an adjuvant for vaccination. Previous murine studies have found that 4-1BB can participate in optimal priming of effector and memory CD8 T cells in response to several virus infections, and concordantly direct stimulation of 4-1BB with agonist reagents effectively boosts the CD8 T cell response against those viruses. In contrast, we recently reported that 4-1BB plays no role in the response to a virulent strain of VACV, questioning whether agonists of 4-1BB will be useful adjuvants for vaccination with VACV vectors. Here we show that agonist anti-4-1BB strongly enhanced the primary viral-specific effector CD8 T cell response during infection with live virulent VACV and attenuated VACV, and during immunization with VACV peptides given in IFA. However, accumulation of memory CD8 T cells was enhanced only following infection with virulent VACV or with peptide vaccination, but not with attenuated VACV, correlating in part with more transient expression of 4-1BB on CD8 T cells with attenuated virus. Our data therefore suggest that 4-1BB may be a promising candidate for targeting as an adjuvant for short-term enhancement of CD8 T cell responses with VACV vaccine strategies, but additional receptors may need to be engaged with 4-1BB to allow long-term CD8 T cell immunity with attenuated VACV vectors.
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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) | 0.001 | 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".