Adaptive NKG2C<sup>+</sup> NK cells in cytomegalovirus seropositive individuals predominantly lack NKR‐P1A receptor expression
Bibliographic record
Abstract
ABSTRACT The impact of cytomegalovirus (CMV) infection in shaping natural killer (NK) cell receptor (NKR) repertoire highlights the importance of NKRs in immunity against CMV. NKR‐P1A (CD161) is an inhibitory NKR, whose expression is lost during CMV infection, but its role in NK cell responses during CMV infection is not known. Here, we show selective expansion of adaptive NKG2C+ NK cells lacking NKR‐P1A receptor (NKR‐P1A‒) due to their increased activation and proliferation compared with NKR‐P1A+ NK cells in CMV‐infected individuals. In vitro stimulation of PBMCs showed similar inherent proliferative capacity in both NKR‐P1A+ versus NKR‐P1A‒ NK cells in steady state and upregulation, but not loss of NKR‐P1A receptor expression, in sorted NK cells. Furthermore, CMV infection induced differential gene expression profiles in NKR‐P1A+ versus NKR‐P1A‒ NK cells, and only NKR‐P1A‒ NK cells exhibited transcriptome signatures associated with adaptive NK cells in CMV‐infected individuals. This study further highlights the impact of CMV infection in shaping NK cell receptor repertoire and exclusion of NK cells that express the NKR‐P1A receptor from the adaptive NKG2C+ NK cell population that expands during CMV infection.
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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) | 0.004 | 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".