Identification of two MCMV immunoevasins that modulate NK cell recognition via the NKR-P1B:Clr-b axis
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are a subset of innate lymphoid cells (ILC) capable of recognizing pathological target cells through germline-encoded receptor-ligand interactions. Murine cytomegalovirus (MCMV) is a betaherpesvirus that has co-evolved with its natural host, and as such, a significant portion of its genome encodes immunoevasins that directly target NK cell receptor-ligand interactions. Here, we identify two putative immunoevasins that modulate host-pathogen interactions via the inhibitory NKR-P1B:Clr-b recognition system. First, we identify an m145 family member, m153, that actively prevents MCMV infection-mediated Clr-b downregulation. Ectopic expression of m153 in mouse fibroblasts increases Clr-b expression at the cell surface. In contrast, infection with a virus deficient in m153 shows more substantial Clr-b loss at the cell surface compared to wild-type MCMV. Importantly, enhanced Clr-b loss upon infection with the m153-mutant could be reversed upon m153 complementation by overexpression. Secondly, we have identified an m02 family member, m12, that directly interacts with the NKR-P1B inhibitory receptor in reporter cell assays. Notably, the interaction between NKR-P1B and m12 is both Clr-b/β2m-independent, and infection of fibroblasts with m12-mutant MCMV abrogates NKR-P1B ligation on reporter cells. This interaction, suggestive of decoy ligand function, is also both MCMV strain-dependent and host NKR-P1B allele-specific, suggesting that polymorphisms have evolved at both the pathogen and host levels that affect NK cell recognition of infected target 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) | 0.001 | 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".