What Defines NK Cell Functional Fate: Phenotype or Metabolism?
Bibliographic record
Abstract
NK cells are capable of an array of functions that range widely from their classic anti-tumor and anti-viral cytotoxic effector functions, to their critical regulatory roles in controlling inflammatory immune responses and promoting tissue growth. However, the mechanisms that polarize NK cells to these distinct and opposing functions are incompletely understood. NK cell functional subsets are primarily identified and studied based on phenotype, which has served as an accessible means for profiling NK cells and does offer information on NK cell activation state. However, inconsistencies have emerged in using classic phenotypes to inform function, which raise the questions: Can phenotype in fact define NK cell functional fate? What factors do profile and drive NK cell fate? In other immune cells, cell metabolism has been shown to critically determine subset polarization. There is a growing body of evidence that cell metabolism is integral to NK cell effector functions. Glucose-driven glycolysis and oxidative metabolism have been shown to drive classic NK cell anti-tumor and anti-viral effector functions. Recent studies have uncovered a critical role for metabolism in NK cell development, education, and memory generation. In this review, we will draw on the evidence to date to investigate the relationship between NK cell phenotype, metabolism, and functional fate. We explore a paradigm in which the differential activity of metabolic pathways within NK cells produce distinct metabolic fingerprints that comprehensively distinguish and drive the range of NK cell functional abilities. We will discuss future areas of study that are needed to develop and test this paradigm and suggest strategies to efficiently profile NK cells based on metabolism. Given the emerging role of metabolism in driving NK cell fates, profiling and modulating NK cell metabolism holds profound therapeutic potential to tune inflammatory and regulatory NK cell responses to treat disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".