Viral MHC Class I–like Molecule Allows Evasion of NK Cell Effector Responses In Vivo
Bibliographic record
Abstract
The outcome of mouse CMV (MCMV) infection varies among different inbred mouse strains depending on NK cell effector functions governed through recognition receptor triggering. NK cells from different mouse strains possess diverse repertoires of activating or inhibitory Ly49 receptors, which share some of their polymorphic MHC class I (MHC-I) ligands. By examining the NK cell response to MCMV infection in novel BALB substrains congenic for different MHC (or H-2 in mice) haplotypes, we show that recognition of viral MHC-I-like protein m157 by inhibitory Ly49C receptor allows escape from NK cell control of viral replication. Dominant inhibition by Ly49C bound to self-H-2(b) encoded MHC-I molecules masks this effect, which only becomes apparent in distinct H-2 haplotypes, such as H-2(f). The recognition of m157-expressing cells by Ly49C resulted in both decreased NK cell killing in vitro and reduced rejection in vivo. Further, control of infection with m157-deletant (Δm157) MCMV was improved in mice carrying H-2 molecules unrecognized by Ly49C but allowing expansion of NK cell effectors expressing activating Ly49L receptors. Hence, our study is the first, to our knowledge, to demonstrate that MHC-I mimicry strategies used by MCMV to avoid NK cell control are biologically relevant during in vivo viral infection. Of value for human studies is that only a few genetic assortments conditional on the repertoires of viral MHC-I-like proteins/host NK receptors/MHC haplotypes should allow efficient protection against CMV 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) | 0.000 | 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".