Identification of mouse cytomegalovirus immunoevasins that modulate NK cell recognition via the NKR-P1B:Clr-b axis (INM8P.403)
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. Mouse cytomegalovirus (MCMV) is a beta-herpesvirus that has co-evolved with its murine 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 that actively prevents MCMV infection-mediated Clr-b downregulation. Ectopic expression of this m145 family member in mouse fibroblasts increases Clr-b expression at the cell surface. In contrast, infection with an m145-family deletion mutant shows more substantial Clr-b loss at the cell surface compared to wild-type MCMV infection. Importantly, enhanced Clr-b loss upon infection with the deletion mutant could be reversed by complementation experiments involving overexpression of the m145 family immunoevasin. Secondly, we have identified an immunoevasin that directly interacts with the NKR-P1B inhibitory receptor, suggestive of decoy ligand function. This interaction is 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".