Differential effect of GITR on lung effector T cell subpopulations during influenza infection
Bibliographic record
Abstract
Abstract Influenza remains an important global threat. Tissue resident memory T cells (Trm) are critical in protection against influenza, and it is important to identify key mechanisms regulating their formation and persistence. GITR, an NF-κB activating TNFR family member, is required on CD8 T cells for maximal responses against influenza. Our recent work (Mucosal Immunology, doi:10.1038/s41385-018-0105-5) provided evidence that GITRL on monocyte-derived inflammatory APCs provides crucial signals through GITR on T cells in the lung tissue (termed Signal 4), allowing effector T cell accumulation and optimal Trm formation during influenza infection in mice. During influenza infection, responding lung T cells are heterogeneous, some will give rise to terminally differentiated effector cells, while others will become Trm. It remains unknown how GITR affects these subpopulations. To address this issue, we characterized these CD8 T cell subsets using markers Ly6C, KLRG1, T-bet, and the chemokine receptor CX3CR1. By staining with Ly6C and CX3CR1, we identified three different CD8 T cell subsets with differential expression of Ly6C and CX3CR1 (Ly6ChiCX3CR1 hi, Ly6ChiCX3CR1 lo, Ly6CloCX3CR1 lo). Ly6ChiCX3CR1 hi CD8 T cell subset likely represents the most terminally differentiated effector cells as they have the highest expression of KLRG1 and T-bet. Using the adoptive transfer of transgenic OT-I cells, we discovered that there is a larger loss of the Ly6CloCX3CR1 loCD8 T cell in the absence of GITR when compared to the other two subsets. We are currently investigating which of the three subsets give rise to the lung Trm population after influenza infection and the timing and nature of the GITR-dependent signals involved. Funded by CIHR:FDN143250
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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".