The m153 gene product stabilizes expression of the inhibitory NKR-P1B ligand, Clr-b, during mouse cytomegalovirus infection
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are a subset of innate lymphoid cells (ILC) capable of recognizing stressed and infected cells through multiple germline-encoded receptor-ligand interactions. Missing-self recognition involves NK cell sensing of the loss of host-encoded inhibitory ligands on target cells, including MHC class I (MHC-I) molecules and MHC-independent ligands. Mouse cytomegalovirus (MCMV) infection has been shown to promote a rapid loss of the inhibitory NKR-P1B ligand, Clr-b, on infected cells. Here, we provide evidence that an MCMV m145 family member, m153, functions to stabilize Clr-b at the cell surface during MCMV infection. Ectopic expression of m153 in fibroblasts significantly augments Clr-b cell surface levels. Moreover, infections using m153-deficient MCMV mutants (Δm144-m158; Δm153) show an accelerated and exacerbated Clr-b downregulation. Importantly, enhanced loss of Clr-b upon infection with MCMV Δm153-mutants can be reverted to wild-type levels by exogenous m153 complementation in fibroblasts. While the effects of m153 on Clr-b levels are independent of Clec2d transcription, imaging experiments reveal that the m153 and Clr-b proteins only minimally co-localize within the same subcellular compartments, and tagged versions of the proteins were refractory to co-immunoprecipitation using gentle detergents. Indeed, a prominent intracellular vesicular localization of m153 suggests that its effects on Clr-b stabilization may be indirect. In vivo, the Δm153-mutant possesses enhanced virulence, independent of Clr-b and NKR-P1B, suggesting that m153 may modulate other Clr or activating NKR:ligand interactions.
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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 | 0.001 |
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".