CD56 does not contribute to the antitumor, tissue homing, and glycolytic capacity of human NK cells
Bibliographic record
Abstract
Natural killer (NK) cells are critical innate immune cells involved in the clearance of virally infected and malignant cells. Human NK cells are distinguished by their surface expression of CD56 and a lack of CD3. While CD56 expression and cell surface density has long been used as the prototypic marker to characterize primary human NK cell functional subsets, the exact functional role of CD56 in primary human NK cells is still not fully understood. Here, we eliminated the expression of CD56 in human ex vivo expanded NK cells (CD56bright) using CRISPR/Cas9 in order to assess the function of CD56 in this highly activated and cytotoxic NK cell population. We show that the expression of CD56 has no effect on NK cell proliferative capacity or expression of various activation and inhibitory markers. Further, CD56 does not contribute to NK cell-mediated cytotoxicity, inflammatory cytokine production, or the ability of NK cells to control tumor engraftment in vivo. We also found that while deletion of CD56 did not impact NK cell glycolytic metabolism, it did increase NK cell reliance on oxidative phosphorylation. Last, CD56 does not alter expanded NK cell in vivo tissue trafficking. Our results indicate that while CD56 expression could be used to indicate a hyperfunctional state of NK cells, it does not directly influence the antitumor functions of expanded NK cells.
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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".