Specific Dysregulation of IFNγ Production by Natural Killer Cells Confers Susceptibility to Viral Infection
Bibliographic record
Abstract
Natural Killer (NK) cells contribute to the control of viral infection by directly killing target cells and mediating cytokine release. In C57BL/6 mice, the Ly49H activating NK cell receptor plays a key role in early resistance to mouse cytomegalovirus (MCMV) infection through specific recognition of the MCMV-encoded MHC class I-like molecule m157 expressed on infected cells. Here we show that transgenic expression of Ly49H failed to provide protection against MCMV infection in the naturally susceptible A/J mouse strain. Characterization of Ly49H(+) NK cells from Ly49h-A transgenic animals showed that they were able to mount a robust cytotoxic response and proliferate to high numbers during the course of infection. However, compared to NK cells from C57BL/6 mice, we observed an intrinsic defect in their ability to produce IFNγ when challenged by either m157-expressing target cells, exogenous cytokines or chemical stimulants. This effect was limited to NK cells as T cells from C57BL/6 and Ly49h-A mice produced comparable cytokine levels. Using a panel of recombinant congenic strains derived from A/J and C57BL/6 progenitors, we mapped the genetic basis of defective IFNγ production to a single 6.6 Mb genetic interval overlapping the Ifng gene on chromosome 10. Inspection of the genetic interval failed to reveal molecular differences between A/J and several mouse strains showing normal IFNγ production. The chromosome 10 locus is independent of MAPK signalling or decreased mRNA stability and linked to MCMV susceptibility. This study highlights the existence of a previously uncovered NK cell-specific cis-regulatory mechanism of Ifnγ transcript expression potentially relevant to NK cell function in health and disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".