Chronic Ethanol Exposure Selectively Inhibits the Influenza‐Specific <scp>CD</scp>8 T Cell Response During Influenza A Virus Infection
Bibliographic record
Abstract
BACKGROUND: It is well established that chronic ethanol (EtOH) consumption is associated with increased incidence and disease severity of respiratory infections. Our recent work demonstrates this increase in disease severity to influenza A virus (IAV) infections is due, in part, to a failure to mount a robust IAV-specific CD8 T cell response along with a specific impairment in the ability of these T cells to produce interferon γ (IFNγ). However, the full extent of the lesion in the effector CD8 T cell compartment during chronic EtOH consumption remains unknown. METHODS: Utilizing the Meadows-Cook murine model of chronic alcohol consumption, mice received EtOH in their drinking water for 8 or 12 weeks. Mice were challenged intranasally with IAV, and the activation and effector functions of IAV-specific CD8 T cells were determined in both the lung-draining lymph nodes (dLN) and lungs. RESULTS: Our results confirm the defect in IFNγ production; however, the ability of IAV-specific T cells to produce tumor necrosis factor α (TNFα) and interleukin-2 (IL-2) in EtOH-consuming mice remains unaltered. In contrast, EtOH consumption significantly reduces the ability of CD8 T cells to degranulate and kill IAV-specific targets. Finally, our findings suggest the lesion begins during the initial activation of CD8 T cells, as we observe early defects in proliferation in the dLN of IAV-infected, EtOH-consuming mice. CONCLUSIONS: These findings highlight the previously unrecognized depth of the lesion in the IAV-specific CD8 T cell response during chronic EtOH consumption. Given the important role CD8 T cell immunity plays in control of IAV, these findings may aid in the development of vaccination and/or therapeutic strategies to reverse these defects in the CD8 T cell response and reduce serious disease outcomes associated with IAV infections in alcoholics.
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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".