Survival of Effector CD8+ T Cells during Influenza Infection Is Dependent on Autophagy
Bibliographic record
Abstract
The activation and expansion of effector CD8(+) T cells are essential for controlling viral infections and tumor surveillance. During an immune response, T cells encounter extrinsic and intrinsic factors, including oxidative stress, nutrient availability, and inflammation, that can modulate their capacity to activate, proliferate, and survive. The dependency of T cells on autophagy for in vitro and in vivo activation, expansion, and memory remains unclear. Moreover, the specific signals and mechanisms that activate autophagy in T effector cells and their survival are not known. In this study, we generated a novel inducible autophagy knockout mouse to study T cell effector responses during the course of a virus infection. In response to influenza infection, Atg5(-/-) CD8(+) T cells had a decreased capacity to reach the peak effector response and were unable to maintain cell viability during the effector phase. As a consequence of Atg5 deletion and the impairment in effector-to-memory cell survival, mice fail to mount a memory response following a secondary challenge. We found that Atg5(-/-) effector CD8(+) T cells upregulated p53, a transcriptional state that was concomitant with widespread hypoxia in lymphoid tissues of infected mice. The onset of p53 activation was concurrent with higher levels of reactive oxygen species (ROS) that resulted in ROS-dependent apoptotic cell death, a fate that could be rescued by treating with the ROS scavenger N-acetylcysteine. Collectively, these results demonstrate that effector CD8(+) T cells require autophagy to suppress cell death and maintain survival in response to a viral infection.
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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) | 0.000 | 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".