Comparison of primary human cytotoxic T-cell and natural killer cell responses reveal similar molecular requirements for lytic granule exocytosis but differences in cytokine production
Bibliographic record
Abstract
Cytotoxic lymphocytes, encompassing cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, kill pathogen-infected, neoplastic, or certain hematopoietic cells through the release of perforin-containing lytic granules. In the present study, we first performed probability-state modeling of differentiation and lytic granule markers on CD8(+) T cells to enable the comparison of bona fide CTLs with NK cells. Analysis identified CD57(bright) expression as a reliable phenotype of granule marker-containing CTLs. We then compared CD3(+)CD8(+)CD57(bright) CTLs with NK cells. Healthy adult peripheral blood CD3(+)CD8(+)CD57(bright) CTLs expressed more granzyme B but less perforin than CD3(-)CD56(dim) NK cells. On stimulation, such CTLs degranulated more readily than other T-cell subsets, but had a propensity to degranulate that was similar to NK cells. Remarkably, the CTLs produced cytokines more rapidly and with greater frequency than NK cells. In patients with biallelic mutations in UNC13D, STX11, or STXBP2 associated with familial hemophagocytic lymphohistiocytosis, CTL and NK cell degranulation were similarly impaired. Therefore, cytotoxic lymphocyte subsets have similar requirements for Munc13-4, syntaxin-11, and Munc18-2 in lytic granule exocytosis. The present results provide a detailed comparison of human CD3(+)CD8(+)CD57(bright) CTLs and NK cells and suggest that analysis of CD57(bright) CTL function may prove useful in the diagnosis of primary immunodeficiencies including familial hemophagocytic lymphohistiocytosis.
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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 | 0.000 |
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 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".