Inflammatory impact of IFN-γ in CD8<sup>+</sup>T cell-mediated lung injury is mediated by both Stat1-dependent and -independent pathways
Bibliographic record
Abstract
Influenza infection results in considerable pulmonary pathology, a significant component of which is mediated by CD8(+) T cell effector functions. To isolate the specific contribution of CD8(+) T cells to lung immunopathology, we utilized a nonviral murine model in which alveolar epithelial cells express an influenza antigen and injury is initiated by adoptive transfer of influenza-specific CD8(+) T cells. We report that IFN-γ production by adoptively transferred influenza-specific CD8(+) T cells is a significant contributor to acute lung injury following influenza antigen recognition, in isolation from its impact on viral clearance. CD8(+) T cell production of IFN-γ enhanced lung epithelial cell expression of chemokines and the subsequent recruitment of inflammatory cells into the airways. Surprisingly, Stat1 deficiency in the adoptive-transfer recipients exacerbated the lung injury that was mediated by the transferred influenza-specific CD8(+) T cells but was still dependent on IFN-γ production by these cells. Loss of Stat1 resulted in sustained activation of Stat3 signaling, dysregulated chemokine expression, and increased infiltration of the airways by inflammatory cells. Taken together, these data identify important roles for IFN-γ signaling and Stat1-independent IFN-γ signaling in regulating CD8(+) T cell-mediated acute lung injury. This is the first study to demonstrate an anti-inflammatory effect of Stat1 on CD8(+) T cell-mediated lung immunopathology without the complication of differences in viral load.
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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 | 0.000 | 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) | 0.002 | 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".