Interferon-Dependent Induction of Clr-b during Mouse Cytomegalovirus Infection Protects Bystander Cells from Natural Killer Cells via NKR-P1B-Mediated Inhibition
Bibliographic record
Abstract
Natural killer (NK) cells are innate lymphocytes that aid in self-nonself discrimination by recognizing cells undergoing pathological alterations. The NKR-P1B inhibitory receptor recognizes Clr-b, a self-encoded marker of cell health downregulated during viral infection. Here, we show that Clr-b loss during mouse cytomegalovirus (MCMV) infection is predicated by a loss of Clr-b (Clec2d) promoter activity and nascent transcripts, driven in part by MCMV ie3 (M122) activity. In contrast, uninfected bystander cells near MCMV-infected fibroblasts reciprocally upregulate Clr-b expression due to paracrine type-I interferon (IFN) signaling. Exposure of fibroblasts to type-I IFN augments Clec2d promoter activity and nascent Clr-b transcripts, dependent upon a cluster of IRF3/7/9 motifs located ∼200 bp upstream of the transcriptional start site. Cells deficient in type-I IFN signaling components revealed IRF9 and STAT1 as key transcription factors involved in Clr-b upregulation. In chromatin immunoprecipitation experiments, the Clec2d IRF cluster recruited STAT2 upon IFN-α exposure, confirming the involvement of ISGF3 (IRF9/STAT1/STAT2) in positively regulating the Clec2d promoter. These findings demonstrate that Clr-b is an IFN-stimulated gene on healthy bystander cells, in addition to a missing-self marker on MCMV-infected cells, and thereby enhances the dynamic range of innate self-nonself discrimination by NK 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 | 0.001 |
| 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".