Virus stimulation of human mast cells results in the recruitment of CD56 <sup>+</sup> T cells by a mechanism dependent on CCR5 ligands
Bibliographic record
Abstract
The trafficking of effector cells to sites of infection is crucial for antiviral responses. However, the mechanisms of recruitment of the interferon-γ-producing and cytotoxic CD56(+) T cells are poorly understood. Human mast cells are sentinel cells found in the skin and airway and produce selected proinflammatory mediators in response to multiple pathogen-associated signals. The role of human mast cell-derived chemokines in T-cell recruitment to virus infection was examined. Supernatants from primary human cord blood-derived mast cells (CBMCs) infected with mammalian reovirus were examined for chemokine production and utilized in chemotaxis assays. Virus-infected CBMCs produced several chemokines, including CCL3, CCL4, and CCL5. Supernatants from reovirus-infected CBMCs selectively induced the chemotaxis of CD8(+) T cells (10±1%) and CD3(+)CD56(+) T cells (19±5%). CD56(+) T-cell migration was inhibited by pertussis toxin (65±9%) and met-RANTES (56±7%), a CCR1/CCR5 antagonist. CD56(+) T cells expressed CCR5, but little CCR1. The depletion of CCL3, CCL4, and CCL5 from reovirus-infected CBMC supernatants significantly (41±10%) inhibited CD56(+) T-cell chemotaxis. This study demonstrates a novel role for mast cells and CCR5 in CD56(+) T-cell trafficking and suggests that human mast cells enhance immunity to viruses through the selective recruitment of cytotoxic effector cells to virus infection sites. These findings could be exploited to enhance local T-cell responses in chronic viral infection and malignancies at mast cell-rich sites.
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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 | 0.001 | 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".