Intrinsic TNF/TNFR2 Interactions Fine-Tune the CD8 T Cell Response to Respiratory Influenza Virus Infection in Mice
Bibliographic record
Abstract
TNF is an important inflammatory mediator and a target for intervention. TNF is produced by many cell types and is involved in innate inflammation as well as adaptive immune responses. CD8 T cells produce TNF and can also respond to TNF. Deficiency of TNF or TNFR2 has been shown to affect anti-viral immunity. However, as the complete knockout of TNF or its receptors has effects on multiple cell types as well as on lymphoid architecture, it has been difficult to assess the role of TNF directly on T cells during viral infection. Here we have addressed this issue by analyzing the effect of CD8 T cell intrinsic TNF/TNFR2 interactions during respiratory influenza infection in mice, using an adoptive transfer model in which only the T cells lack TNF or TNFR2. During a mild influenza infection, the capacity of the responding CD8 T cells to produce TNF increases from day 6 through day 12, beyond the time of viral clearance. Although T cell intrinsic TNF is dispensable for initial expansion of CD8 T cells up to day 9 post infection, intrinsic TNF/TNFR2 interactions potentiate contraction of the CD8 T cell response in the lung between day 9 and 12 post infection. On the other hand, TNF or TNFR2-deficient CD8 T cells in the lung express lower levels of IFN-γ and CD107a per cell than their wild type counterparts. Comparison of TNF levels on the TNFR2 positive and negative T cells is consistent with TNF/TNFR2 interactions inducing feedback downregulation of TNF production by T cells, with greater effects in the lung compared to spleen. Thus CD8 T cell intrinsic TNF/TNFR2 interactions fine-tune the response to influenza virus in the lung by modestly enhancing effector functions, but at the same time potentiating the contraction of the CD8 T cell response post-viral clearance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".