Porcine γδ T cells express cytotoxic cell-associated markers and display killing activity but are not selectively cytotoxic against PRRSV- or swIAV-infected macrophages
Bibliographic record
Abstract
Background: Gamma-delta (γδ) T cells are a major immune cell subset in pigs. Approximately 50% of circulating T cells are γδ T cells in young pigs and up to 30% in adult sows. Despite this abundance, the functions of porcine γδ T cells are mostly unidentified. In humans and mice, activated γδ T cells exhibit broad innate cytotoxic activity against a wide variety of stressed, infected, and cancerous cells through death receptor/ligand-dependent and perforin/granzyme-dependent pathways. However, so far, it is unknown whether porcine γδ T cells have the ability to perform cytotoxic functions. Methods: cytotoxicity assays were performed using purified γδ T cells as effector cells and virus-exposed or mock-treated primary porcine alveolar macrophages as target cells. Results: γδ T cells express cytotoxic markers (CD16, NKp46, perforin) with higher perforin and NKp46 expression in γδ T cells isolated from lung and nasal mucosa. Moreover, we found that γδ T cells can exhibit cytotoxic functions in a cell-cell contact and degranulation-dependent manner. However, porcine γδ T cells did not seem to specifically target Porcine Reproductive and Respiratory Syndrome Virus or swine Influenza A Virus-infected macrophages, which may be due to viral escape mechanisms. Conclusion: Porcine γδ T cells express cytotoxic markers and can exhibit cytotoxic activity in vitro. The specific mechanisms by which porcine γδ T cells recognize target cells are not fully understood but may involve the detection of cellular stress signals.
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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".