Distinct MHC class I–dependent NK cell–activating receptors control cytomegalovirus infection in different mouse strains
Bibliographic record
Abstract
Recognition of mouse cytomegalovirus (MCMV)-infected cells by activating NK cell receptors was first described in the context of Ly49H, which confers resistance to C57BL/6 mice. We investigated the ability of other activating Ly49 receptors to recognize MCMV-infected cells in mice from various H-2 backgrounds. We observed that Ly49P1 from NOD/Ltj mice, Ly49L from BALB mice, and Ly49D2 from PWK/Pas mice respond to MCMV-infected cells in the context of H-2D(k) and the viral protein m04/gp34. Recognition was also seen in the H-2(d) and/or H-2(f) contexts, depending on the Ly49 receptor examined, but never in H-2(b). Furthermore, BALB.K (H-2(k)) mice showed reduced viral loads compared with their H-2(d) or H-2(b) congenic partners, a reduction which was dependent on interferon γ secretion by Ly49L(+) NK cells early after infection. Adoptive transfer of Ly49L(+), but not Ly49L(-), NK cells significantly increased resistance against MCMV infection in neonate BALB.K mice. These results suggest that multiple activating Ly49 receptors participate in H-2-dependent recognition of MCMV infection, providing a common mechanism of NK cell-mediated resistance against viral infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".