Modulation of Clr Ligand Expression and NKR-P1 Receptor Function during Murine Cytomegalovirus Infection
Bibliographic record
Abstract
Viruses are known to induce pathological cellular states that render infected cells susceptible or resistant to immune recognition. Here, we characterize an MHC-I-independent natural killer (NK) cell recognition mechanism that involves modulation of inhibitory NKR-P1B:Clr-b receptor-ligand interactions in response to mouse cytomegalovirus (MCMV) infection. We demonstrate that mouse Clr-b expression on healthy cells is rapidly lost at the cell surface and transcript levels in a time- and dose-dependent manner upon MCMV infection. In addition, cross-species infections using rat cytomegalovirus (RCMV) infection of mouse fibroblasts and MCMV infection of rat fibroblasts suggest that this response is conserved during host-pathogen interactions. Active viral infection appears to be necessary for Clr-b loss, as cellular stimulation using UV-inactivated whole virus or agonists of many innate pattern recognition receptors failed to elicit efficient Clr-b downregulation. Notably, Clr-b loss could be partially blocked by titrated cycloheximide treatment, suggesting that early viral or nascent host proteins are required for Clr-b downregulation. Interestingly, reporter cell assays suggest that MCMV may encode a novel Clr-b-independent immunoevasin that functionally engages the NKR-P1B receptor. Together, these data suggest that Clr-b modulation is a conserved innate host cell response to virus infection that is subverted by multiple CMV immune evasion strategies.
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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".