4-1BB Regulates Effector CD8 T Cell Accumulation in the Lung Tissue through a TRAF1-, mTOR-, and Antigen-Dependent Mechanism to Enhance Tissue-Resident Memory T Cell Formation during Respiratory Influenza Infection
Bibliographic record
Abstract
The TNFR superfamily member 4-1BB is important in the establishment of tissue-resident memory T cells (Trm) in the lung tissue following influenza infection. Moreover, supraphysiological boosting of 4-1BB in the airways during the boost phase of a prime-boost immunization regimen increases the long-lived Trm population, correlating with increased protection against heterotypic challenge. However, little is known about how 4-1BB contributes to the establishment of the lung Trm population. In this study, we show that effects of 4-1BB on lung Trm accumulation are already apparent at the effector stage, suggesting that the major role of 4-1BB in Trm formation is to allow persistence of CD8 T effector cells in the lung as they transition to Trm. Using supraphysiological stimulation of 4-1BB in the boost phase of a prime-boost immunization, we show that the effect of 4-1BB on Trm generation requires local delivery of both Ag and costimulation, is inhibited by rapamycin treatment during secondary CD8 effector T cell expansion, and is dependent on the signaling adaptor TRAF1. The decrease in lung Trm following early rapamycin treatment is accompanied by increased circulating memory T cells, as well as fewer effectors, suggesting a role for mammalian target of rapamycin (mTOR) in the formation of Trm through effects on the accumulation of effector precursors. Taken together, these data point to an important role for 4-1BB, TRAF1, and mTOR in the persistence of CD8 effector T cells in the lung parenchyma, thereby allowing the transition to Trm.
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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 | 0.001 |
| 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".