CD56-negative NK cells: Frequency in peripheral blood, expansion during HIV-1 infection, functional capacity, and KIR expression
Bibliographic record
Abstract
Human NK cells are usually defined as CD3-CD56+lymphocytes. However, a CD56-CD16+(CD56neg) lymphocyte population that displays NK-associated markers expands during chronic viral infections such as HIV-1 and HCV, and, to lesser extent, in herpesvirus infections. This CD56neg NK cell subset has been understudied because it requires the exclusion of other lymphocytes to accurately identify its presence. Many questions remain regarding the origin, development, phenotype, and function of the CD56neg NK cell population. Our objective was to determine the frequency of this NK subset in healthy controls and its alteration in viral infections by performing a meta-analysis. In addition to this, we analyzed deposited CyTOF and scRNAseq datasets to define the phenotype and subsets of the CD56neg NK cell population, as well as their functional variation. We found in 757 individuals, from a combined 28 studies and 6 datasets, that the CD56neg subset constitutes 5.67% of NK cells in healthy peripheral blood, while HIV-1 infection increases this population by a mean difference of 10.69%. Meta-analysis of surface marker expression between NK subsets showed no evidence of increased exhaustion or decreased proliferation within the CD56neg subset. CD56neg NK cells have a distinctive pattern of KIR expression, implying they have a unique potential for KIR-mediated education. A perforin-CD94-NKG2C-NKp30-CD56neg population exhibited different gene expression and degranulation responses against K562 cells compared to other CD56neg cells. This analysis distinguishes two functionally distinct subsets of CD56neg NK cells. They are phenotypically diverse and have differing capacity for education by HLA class-I interactions with KIRs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".