Development of Cell-Based Tuberculosis Vaccines: Genetically Modified Dendritic Cell Vaccine Is a Much More Potent Activator of CD4 and CD8 T Cells Than Peptide- or Protein-Loaded Counterparts
Bibliographic record
Abstract
Genetically modified dendritic cell (DC)-based vaccines have not been explored for immunization against tuberculosis. A gene-modified DC vaccine expressing Mycobacterium tuberculosis (M.tb) antigen 85A (Ag85A) was developed by using a recombinant replication-deficient adenoviral gene transfer vector (AdAg85A). AdAg85A-transduced DC vaccine (AdAg85/DC) expressed higher levels of IL-12 and was much more immunogenic than Ag85 protein-loaded (pro/DC) or CD4/CD8 T cell peptide-loaded (pep/DC) DC vaccines. Compared to pro/DC or pep/DC, AdAg85/DC elicited a remarkably higher level of ex vivo IFN-gamma production by CD4 and CD8 T cells at weeks 2, 6, and 12 postimmunization, which was coupled with higher frequencies of antigen-specific T cells. By an in vivo CD8 or CD4 T cell cytotoxicity (CTL) assay, AdAg85/DC was shown to provoke much higher and more sustained levels of CD8 and CD4 CTL activity up to 12 weeks postimmunization. Intramuscular (im) AdAg85/DC immunization was more potent than the iv route of AdAg85/DC immunization. Such stronger immunogenicity of im AdAg85/DC vaccination was corroborated with better protection from M.tb challenge. Our results thus suggest that genetically modified DC-based TB vaccine is superior to subunit DC vaccines and has the potential for therapeutic applications.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".