Optimal TLR9 signal converts tolerogenic CD4–8– DCs into immunogenic ones capable of stimulating antitumor immunity via activating CD4+ Th1/Th17 and NK cell responses
Bibliographic record
Abstract
Abstract TLR ligands have been reported to promote DC maturation and enhance CD8+ CTL responses. We have demonstrated previously that CD4-8- DCs secreting TGF-beta stimulate CD4+ Tr1 cell responses. Here, we have assessed whether TLR4 and TLR9 signaling through LPS and CpG stimulation can convert CD4-8- DC-induced tolerance. We demonstrate that immature OVA-pulsed CD4-8- DCs cultured in medium with LPS (2 microg/ml) and CpG (5 microg/ml) for 8 h became mature DCs (DCOVA) with no TGF-beta secretion. CpG-treated, CD4-8- DCOVA-secreting IL-6/IL-15 induced IFN-gamma/IL-17-secreting/T-bet- and ROR-gammat-expressing CD4+ Th1/Th17, whereas LPS-treated CD4-8- DCOVA stimulated IFN-gamma-secreting/T-bet-expressing CD4+ Th1 responses. The former also significantly stimulated more efficient OVA-specific CD8+ T cell responses and antitumor immunity against OVA-expressing BL6-10OVA tumor cells than the latter (P<0.05). CpG-treated, CD4-8- DCOVA-stimulated CD4+ Th1/Th17 cell responses and antitumor immunity were found to be reduced by using neutralizing anti-IL-6, IL-15, and NK1.1 antibodies in wild-type C57BL/6 mice, IL-15R-/- mice for immunization, or CD4-8- (IL-6-/-) DCOVA for immunization in C57BL/6 mice. Interestingly, in vitro-generated CD4+ Th17 cells significantly enhanced LPS-treated, CD4-8- DCOVA-induced in vivo antitumor immunity via increasing CD8+ CTL responses (P<0.05), although they did not show any direct killing activity against tumor cells in vitro. In addition, prolonged 48 h CpG-treated CD4-8- DCOVA dramatically diminished its cytokine secretion, stimulatory effect, and antitumor immunity. Taken together, our data demonstrate an effect of conversion of tolerogenic DCs into immunogenic ones capable of stimulating antitumor immunity via activating CD4+ Th1/Th17 and NK cell responses by optimal CpG signaling, which may advance current understanding of the importance of TLR9 signaling in a DC-based cancer vaccine.
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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) | 0.003 | 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".