Differential Activation of Dendritic Cells by Toll‐like Receptor Agonists Isolated from the Gram‐positive Vaccine Vector <i>Streptococcus gordonii</i>
Bibliographic record
Abstract
The oral commensal bacterium Streptococcus gordonii has been gathering interest as a candidate live mucosal vaccine delivery vector. S. gordonii has been shown to be capable of activating antigen presenting immune cells in a manner which leads to their activation and maturation, yet the mechanism used by S. gordonii to do so is poorly understood. The aim of this work was to investigate the immunostimulatory components of S. gordonii in inducing murine dendritic cell (DC) activation and maturation. Lipoteichoic acid (LTA), lipoprotein (LP), peptidoglycan (PGN), and DNA were isolated from S. gordonii, and used to stimulate murine DC. Cytokine production and DC surface marker upregulation in response to the bacterial components was quantified by enzyme-linked immunosorbent assay and flow cytometry respectively. The results were contrasted against data obtained from DC derived from MyD88, TRIF [TIR(Toll/Interleukin-1 Receptor)-domain-containing adapter-inducing interferon-beta] or toll-like receptor-2 (TLR-2) knockout mice. The four S. gordonii bacterial components were found to differentially induce cytokine production and surface marker upregulation by murine DC. Activation of DC by both whole S. gordonii cells and the four bacterial components was abrogated in the absence of MyD88, but not in the absence of TRIF. LTA, LP and PGN, but not DNA and whole S. gordonii, required TLR-2 to induce a DC response. The results collectively indicate that S. gordonii activates DC predominantly through a MyD88-dependent and TRIF-independent pathway. This activation can be attributed to multiple immunostimulatory components present within S. gordonii bacterial cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".