The role of mTOR, TRAF1, and local antigen in 4-1BB-dependent establishment of Trm in the lung
Bibliographic record
Abstract
Abstract The tumour necrosis factor receptor (TNFR) superfamily receptor 4-1BB is important in the establishment of a tissue-resident memory (Trm) population in the lung tissue following influenza infection. Moreover, supraphysiological boosting of 4-1BB during the boost phase of a prime-boost immunization regimen can greatly enhance the establishment of a long-lived Trm population that protects against lethal heterotypic challenge (Zhou et al. Mucosal Immunology 2017). However, little is known about how 4-1BB contributes to the establishment of the lung Trm population. Here, we investigated the mechanism by which 4-1BB induces lung Trm cells. Using competitive mixed bone marrow chimeras, we found that the effect of 4-1BB on lung resident influenza-specific T cells does not substantially change between day 9 and 45 post-infection, suggesting that the main effect of 4-1BB is to allow the persistence of CD8 T effector cells as they transition to Trm. The signaling adaptor TRAF1, downstream of 4-1BB was also shown to be important in enhancing the numbers of Teffectors and Trm in the lung. Using supraphysiological stimulation of 4-1BB in the boost phase of a prime boost immunization, we show 4-1BB-mediated Trm generation is dependent on local delivery of both antigen and costimulation and that this process is inhibited by rapamycin, suggesting a role for mTOR in this process. Also, using this prime-boost model we demonstrate that TRAF1 contributes to the effect of 4-1BB on Trm generation. Taken together, these data point to an important role for 4-1BB, TRAF1 and mTORC1 in allowing the lung effector T cells in the lung parenchyma to survive through the transition to tissue resident memory T cells. This work was funded by CIHR grant FDN-143250 to THW
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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) | 0.002 | 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".