Boosting Functional Avidity of CD8 <sup>+</sup> T Cells by Vaccinia Virus Vaccination Depends on Intrinsic T-Cell MyD88 Expression but Not the Inflammatory Milieu
Bibliographic record
Abstract
UNLABELLED: T-cell functional avidity is a crucial determinant for efficient pathogen clearance. Although recombinant DNA priming coupled with a vaccinia-vectored vaccine (VACV) boost has been widely used to mount robust CD8+ T-cell responses, how VACV boost shapes the properties of memory CD8+ T cells remains poorly defined. Here, we characterize the memory CD8+ T cells boosted by VACV and demonstrate that the intrinsic expression of MyD88 is critical for their high functional avidity. Independent of selection of clones with high-affinity T-cell receptor (TCR) or of enhanced proximal TCR signaling, the VACV boost significantly increased T-cell functional avidity through a decrease in the activation threshold. VACV-induced inflammatory milieu is not sufficient for this improvement, as simultaneous administration of the DNA vaccine and mock VACV had no effects on the functional avidity of memory CD8+ T cells. Furthermore, reciprocal adoptive transfer models revealed that the intrinsic MyD88 pathway is required for instructing the functional avidity of CD8+ T cells boosted by VACV. Taking these results together, the intrinsic MyD88 pathway is required for the high functional avidity of VACV-boosted CD8+ T cells independent of TCR selection or the VACV infection-induced MyD88-mediated inflammatory milieu. IMPORTANCE: Functional avidity is one of the crucial determinants of T-cell functionality. Interestingly, although it has been demonstrated that a DNA prime-VACV boost regimen elicits high levels of T-cell functional avidity, how VACV changes the low avidity of CD8+ T cells primed by DNA into higher ones in vivo is less defined. Here, we proved that the enhancement of CD8+ T cell avidity induced by VACV boost is mediated by the intrinsic MyD88 pathway but not the MyD88-mediated inflammatory milieu, which might provide prompts in vaccine design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".