Human neutrophils interact with both 6-sulfo LacNAc+ DC and NK cells to amplify NK-derived IFNγ: role of CD18, ICAM-1, and ICAM-3
Bibliographic record
Abstract
The role of neutrophils as key players in the regulation of innate and adaptive immune responses is increasingly being recognized. We report that human neutrophils establish a network with both natural killer (NK) cells and 6-sulfo LacNAc(+) dendritic cells (slanDCs), which ultimately serves to up-regulate NK-derived interferonγ (IFNγ). This network involves direct reciprocal interactions and positive amplification loops mediated by cell-derived cytokines. Accordingly, we show that after lipopolysaccharide + interleukin-2 (IL-2) or IL-15/IL-18 stimulation, neutrophils directly interact with and potentiate the activity of both slanDCs and NK cells. On the one hand, neutrophils augment the release of IL-12p70 by slanDCs via a CD18/ intercellular adhesion molecule-1 (ICAM-1) interaction that stimulates activated NK cells to produce IFNγ. IFNγ further potentiates the interaction between neutrophils and slanDCs and the release of slanDC-derived IL-12p70, thus creating a positive feedback loop. On the other hand, neutrophils directly co-stimulate NK cells via CD18/ICAM-3, leading to the production of IFNγ. Colocalization of neutrophils, NK cells, and slanDCs, as well as of IL-12p70 and IFNγ, in inflamed tissues of Crohn disease and psoriasis provides strong evidence for a novel cellular and cytokine cooperation within the innate immune system in which neutrophils act as amplifiers of NK cell/slanDC-mediated responses.
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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 | 0.000 | 0.000 |
| 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".