Influenza Infection Leads to Increased Susceptibility to Subsequent Bacterial Superinfection by Impairing NK Cell Responses in the Lung
Bibliographic record
Abstract
Influenza viral infection is well-known to predispose to subsequent bacterial superinfection in the lung but the mechanisms have remained poorly defined. We have established a murine model of heterologous infections by an H1N1 influenza virus and Staphylococcus aureus. We found that indeed prior influenza infection markedly increased the susceptibility of mice to secondary S. aureus superinfection. Severe sickness and heightened bacterial infection in flu and S. aureus dual-infected animals were associated with severe immunopathology in the lung. We further found that flu-experienced lungs had an impaired NK cell response in the airway to subsequent S. aureus bacterial infection. Thus, adoptive transfer of naive NK cells to the airway of prior flu-infected mice restored flu-impaired antibacterial host defense. We identified that TNF-alpha production of NK cells played an important role in NK cell-mediated antibacterial host defense as NK cells in flu-experienced lungs had reduced TNF-alpha expression and adoptive transfer of TNF-alpha-deficient NK cells to the airway of flu-infected mice failed to restore flu-impaired antibacterial host defense. Defected NK cell function was found to be an upstream mechanism of depressed antibacterial activities by alveolar macrophages as contrast to naive wild-type NK cells, the NK cells from flu-infected or TNF-alpha-deficient mice failed to enhance S. aureus phagocytosis by alveolar macrophages. Together, our study identifies the weakened NK cell response in the lung to be a novel critical mechanism for flu-mediated susceptibility to bacterial superinfection.
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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 | 0.000 |
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".