Influenza Virus Targets Class I MHC-Educated NK Cells for Immunoevasion
Bibliographic record
Abstract
The immune response to influenza virus infection comprises both innate and adaptive defenses. NK cells play an early role in the destruction of tumors and virally-infected cells. NK cells express a variety of inhibitory receptors, including those of the Ly49 family, which are functional homologs of human killer-cell immunoglobulin-like receptors (KIR). Like human KIR, Ly49 receptors inhibit NK cell-mediated lysis by binding to major histocompatibility complex class I (MHC-I) molecules that are expressed on normal cells. During NK cell maturation, the interaction of NK cell inhibitory Ly49 receptors with their MHC-I ligands results in two types of NK cells: licensed ("functional"), or unlicensed ("hypofunctional"). Despite being completely dysfunctional with regard to rejecting MHC-I-deficient cells, unlicensed NK cells represent up to half of the mature NK cell pool in rodents and humans, suggesting an alternative role for these cells in host defense. Here, we demonstrate that after influenza infection, MHC-I expression on lung epithelial cells is upregulated, and mice bearing unlicensed NK cells (Ly49-deficient NKCKD and MHC-I-deficient B2m-/- mice) survive the infection better than WT mice. Importantly, transgenic expression of an inhibitory self-MHC-I-specific Ly49 receptor in NKCKD mice restores WT influenza susceptibility, confirming a direct role for Ly49. Conversely, F(ab')2-mediated blockade of self-MHC-I-specific Ly49 inhibitory receptors protects WT mice from influenza virus infection. Mechanistically, perforin-deficient NKCKD mice succumb to influenza infection rapidly, indicating that direct cytotoxicity is necessary for unlicensed NK cell-mediated protection. Our findings demonstrate that Ly49:MHC-I interactions play a critical role in influenza virus pathogenesis. We suggest a similar role may be conserved in human KIR, and their blockade may be protective in humans.
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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".