Recruitment and Activation of Natural Killer Cells <i>In vitro</i> by a Human Dendritic Cell Vaccine
Bibliographic record
Abstract
Recruitment of circulating natural killer (NK) cells into inflamed lymph nodes is known to provide a potent, IFN-gamma-dependent boost for Th1-polarized immune responses in mouse models. Such NK cell recruitment into draining lymph nodes is induced by certain s.c. injected adjuvants, including mature vaccine dendritic cells (DC), and is mediated by a CXCR3-dependent pathway. Here, we show that monocyte-derived immature human DCs stimulated with polyinosinic acid:polycytidylic acid, IFN-alpha, tumor necrosis factor-alpha (TNF-alpha), interleukin-1 beta (IL-1 beta), and IFN-gamma, alpha-type 1-polarized DC (alpha DC1), secrete profuse amounts of the CXCR3 ligand CXCL9/MIG and substantial amounts of CXCL10/IP-10 and CXCL11/I-TAC after withdrawal of maturation stimuli. In sharp contrast, no measurable production of these chemokines was found in DCs after maturation with the current gold standard maturation cocktail for human DC-based cancer vaccines consisting of TNF-alpha, IL-1 beta, IL-6, and prostaglandin-E(2) (PGE(2)-DC). PGE(2)-DCs preferentially produced the Th2 and regulatory T-cell-attracting chemokines CCL17/TARC and CCL22/MDC, whereas only marginal levels of these chemokines were produced by alpha DC1s. Functional studies in vitro showed that supernatants from mature alpha DC1s actively recruited CD3(-)CD56(+) NK cells and that adding anti-CXCL9/MIG antibodies to the alpha DC1 supernatant substantially reduced this recruitment. Finally, alpha DC1s were able to induce IFN-gamma production when cocultured with resting autologous NK cells, but only if concurrent CD40 ligation was provided. These novel findings indicate that injected human alpha DC1-based vaccines have the potential to recruit and activate NK cells during their arrival to draining lymph nodes and that this feature may be of relevance for efficient priming of Th1 cells and CTLs.
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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) | 0.002 | 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".