4-1BBL on CCR2-dependent cells selects NP366-specific CD8+ T cells for immunodominance during influenza infection 3086
Bibliographic record
Abstract
Abstract Description Memory CD8+ T cells are crucial for cross-reactive protection against heterosubtypic influenza A virus (IAV) infection. The tumour necrosis factor receptor 4-1BB contributes to accumulation of antigen-specific CD8+ T cells during viral infection, however the pertinent source of its ligand 4-1BBL is unclear. We profiled the innate immune landscape of the murine lung during IAV infection using single-cell RNA sequencing and flow cytometry, identifying inflammatory monocyte-derived antigen-presenting cells (moAPCs) as the predominant expressors of 4-1BBL, with lower expression on classical DC. Given the numerical dominance of moAPCs in tissue during acute infection, we asked whether these two cellular sources of 4-1BBL may differentially impact antigen-specific CD8+ T cell accumulation. Using multiple mouse models of conditional 4-1BBL deletion, we showed that 4-1BBL on moAPCs selects NP366-specific CD8+ T effector and memory cells in the tissue for immunodominance, while 4-1BBL on DCs can support broader epitope specificities. Analysis of 4-1BB wildtype and knockout CD8+ T cells during infection suggests that 4-1BB signaling may enhance survival and mitochondrial fitness, rather than acting as a differentiation cue. Funding Sources Supported by Canadian Institutes for Health Research grants #FDN-143250 and PJT-178020 (to T.H.W), and Ontario Graduate Scholarship and Emerging & Pandemic Infections Consortium Doctoral Award to K.Y. T.H.W. holds the Canada research chair in anti-viral immunity at the University of Toronto. Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)
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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) | 0.003 | 0.002 |
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".