Expansion and Protection by a Virus-Specific NK Cell Subset Lacking Expression of the Inhibitory NKR-P1B Receptor during Murine Cytomegalovirus Infection
Bibliographic record
Abstract
NK cells play a major role in immune defense against human and murine CMV (MCMV) infection. Although the MCMV genome encodes for MHC class I-homologous decoy ligands for inhibitory NK cell receptors to evade detection, some mouse strains have evolved activating receptors, such as Ly49H, to recognize these ligands and initiate an immune response. In this study, we demonstrate that approximately half of the Ly49H-expressing (Ly49H(+)) NK cells in the spleen and liver of C57BL/6 mice also express the inhibitory NKR-P1B receptor. During MCMV infection, the NKR-P1B(-)Ly49H(+) NK cell subset proliferates to constitute the bulk of the NK cell population. This NK cell subset also confers better protection against MCMV infection compared with the NKR-P1B(+)Ly49H(+) subset. The two populations are composed of cells that differ in their surface expression of receptors such as Ly49C/I and NKG2A/C/E, as well as developmental markers, CD27 and CD11b, and the high-affinity IL-2R (CD25) following infection. Although the NKR-P1B(+) NK cells can produce effector molecules such as IFNs and granzymes, their proliferation is inhibited during infection. A similar phenotype in MCMV-infected Clr-b-deficient mice, which lack the ligand for NKR-P1B, suggests the involvement of ligands other than the host Clr-b. Most interestingly, genetic deficiency of the NKR-P1B, but not Clr-b, results in accelerated virus clearance and recovery from MCMV infection. This study is particularly significant because the mouse NKR-P1B:Clr-b receptor:ligand system represents the closest homolog of the human NKR-P1A:LLT1 system and may have a direct relevance to human 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".