Evidence for a cross-talk between human neutrophils and Th17 cells
Bibliographic record
Abstract
Interleukin-17A (IL-17A) and IL-17F are 2 of several cytokines produced by T helper 17 cells (Th17), which are able to indirectly induce the recruitment of neutrophils. Recently, human Th17 cells have been phenotypically characterized and shown to express discrete chemokine receptors, including CCR2 and CCR6. Herein, we show that highly purified neutrophils cultured with interferon-gamma plus lipopolysaccharide produce the CCL2 and CCL20 chemokines, the known ligands of CCR2 and CCR6, respectively. Accordingly, supernatants from activated neutrophils induced chemotaxis of Th17 cells, which was greatly suppressed by anti-CCL20 and anti-CCL2 antibodies. We also discovered that activated Th17 cells could directly chemoattract neutrophils via the release of biologically active CXCL8. Consistent with this reciprocal recruitment, neutrophils and Th17 cells were found in gut tissue from Crohn disease and synovial fluid from rheumatoid arthritis patients. Finally, we report that, although human Th17 cells can directly interact with freshly isolated or preactivated neutrophils via granulocyte-macrophage colony-stimulating factor, tumor necrosis factor-alpha, and interferon-gamma release, these latter cells cannot be activated by IL-17A and IL-17F, because of their lack of IL-17RC expression. Collectively, our results reveal a novel chemokine-dependent reciprocal cross-talk between neutrophils and Th17 cells, which may represent a useful target for the treatment of chronic inflammatory diseases.
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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 | 0.001 | 0.001 |
| 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) | 0.008 | 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".