Low Percentage of Perforin-Expressing NK Cells during Severe SARS-CoV-2 Infection: Consumption Rather than Primary Deficiency
Bibliographic record
Abstract
Genetic defects in the ability to deliver effective perforin have been reported in patients with hemophagocytic lymphohistiocytosis. We tested the hypothesis that a primary perforin deficiency might also be causal in severe SARS-CoV-2 infection. We recruited 54 volunteers confirmed as being SARS-CoV-2-infected by RT-PCR and admitted to intensive care units or non-intensive care units and age- and sex-matched healthy controls. Compared with healthy controls, the percentage of perforin-expressing CD3-CD56+ NK cells quantified by flow cytometry was low in COVID-19 patients (69.9 ± 17.7 versus 78.6 ± 14.6%, p = 0.026). There was no correlation between the proportions of perforin-positive NK cells and T8 lymphocytes. Moreover, the frequency of NK cells producing perforin was neither linked to disease severity nor predictive of death. Although IL-6 is known to downregulate perforin production in NK cells, we did not find any link between perforin expression and IL-6 plasma level. However, we unveiled a negative correlation between the degranulation marker CD107a and perforin expression in NK cells (r = -0.488, p = 10-4). PRF1 gene expression and the frequency of NK cells harboring perforin were normal in patients 1 y after acute SARS-CoV-2 infection. A primary perforin defect does not seem to be a driver of COVID-19 because NK perforin expression is 1) linked neither to T8 perforin expression nor to disease severity, 2) inversely correlated with NK degranulation, and 3) normalized at distance from acute infection. Thus, the cause of low frequency of perforin-positive NK cells appears, rather, to be consumption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".