Toll‐like receptor 2 activation induces C–C chemokine receptor 2‐dependent natural killer cell recruitment to the peritoneum
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are innate effector cells with critical roles not only in tumor immunosurveillance and viral immunity, but also in bacterial and fungal infections. Toll‐like receptor 2 (TLR2) can be important in the early and sustained immune responses to pathogens and tumors through the induction of cytokines and chemokines that recruit and activate immune effector cells. We investigated the role of TLR2 activation in NK cell recruitment with a view to informing approaches to induce or regulate peritoneal NK cell responses therapeutically. Peritoneal injection of TLR2 activators, including peptidoglycan and the lipopeptides FSL‐1 and Pam 3 CSK 4 , resulted in NK cell recruitment after 16 h with increased NK cell numbers maintained for 48 h. TLR2 activators induced large amounts of CCR2 ligands, but much smaller amounts of CCR5 and CXCR3 ligands. Consistent with this observation, NK cell migration was abrogated in CCR2‐deficient mice after peritoneal FSL‐1 injection. Adoptive transfer of CCR2‐deficient NK cells prior to peritoneal FSL‐1 activation confirmed a cell‐intrinsic component of CCR2‐mediated NK cell migration. TLR2 activation did not induce an activated NK cell phenotype, but significant changes included an increase in the KLRG1 + subset and decreased NKG2D expression. Although not activated in vivo , peritoneal NK cells could be activated by interleukin (IL)‐12 and IL‐18 ex vivo to express CD69 and interferonγ. These data demonstrate that TLR2‐mediated immune activation is a potent inducer of NK cell recruitment via a CCR2‐dependent mechanism and that NK cells recruited by this mechanism can respond to additional signals to exert effector cell functions.
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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) | 0.000 | 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) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".