Enhanced Immunogenicity of a Tricomponent Mannan Tetanus Toxoid Conjugate Vaccine Targeted to Dendritic Cells via Dectin-1 by Incorporating β-Glucan
Bibliographic record
Abstract
In a previous attempt to generate a protective vaccine against Candida albicans, a β-mannan tetanus toxoid conjugate showed poor immunogenicity in mice. To improve the specific activation toward the fungal pathogen, we aimed to target Dectin-1, a pattern-recognition receptor expressed on monocytes, macrophages, and dendritic cells. Laminarin, a β-glucan ligand of Dectin-1, was incorporated into the original β-mannan tetanus toxoid conjugate providing a tricomponent conjugate vaccine. A macrophage cell line expressing Dectin-1 was employed to show binding and activation of Dectin-1 signal transduction pathway by the β-glucan-containing vaccine. Ligand binding to Dectin-1 resulted in the following: 1) activation of Src family kinases and Syk revealed by their recruitment and phosphorylation in the vicinity of bound conjugate and 2) translocation of NF-κB to the nucleus. Treatment of immature bone marrow-derived dendritic cells (BMDCs) with tricomponent or control vaccine confirmed that the β-glucan-containing vaccine exerted its enhanced activity by virtue of dendritic cell targeting and uptake. Immature primary cells stimulated by the tricomponent vaccine, but not the β-mannan tetanus toxoid vaccine, showed activation of BMDCs. Moreover, treated BMDCs secreted increased levels of several cytokines, including TGF-β and IL-6, which are known activators of Th17 cells. Immunization of mice with the novel type of vaccine resulted in improved immune response manifested by high titers of Ab recognizing C. albicans β-mannan Ag. Vaccine containing laminarin also affected distribution of IgG subclasses, showing that vaccine targeting to Dectin-1 receptor can benefit from augmentation and immunomodulation of the immune response.
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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) | 0.000 | 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".