GITR differentially affects lung effector T cell subpopulations during influenza virus infection
Bibliographic record
Abstract
Abstract Tissue resident memory T cells (Trm) are critical for local protection against reinfection. The accumulation of T cells in the tissues requires a post-priming signal from TNFR superfamily members, referred to as signal 4. Glucocorticoid-induced TNFR-related protein (GITR; TNFRSF18) signaling is important for this post-priming signal and for Trm formation during respiratory infection with influenza virus. As GITR signaling impacts both effector T cell accumulation and Trm formation, we asked if GITR differentially affects subsets of effector cells with different memory potential. Effector CD4+ T cells can be subdivided into 2 populations based on expression of lymphocyte antigen 6C (Ly6C), whereas effector CD8+ cells can be divided into 3 populations based on Ly6C and CX3CR1. The Ly6Chi and CX3CR1hi T cell populations represent the most differentiated effector T cells. Upon transfer, the Ly6Clo CD4+ effector T cells preferentially enter the lung parenchyma, compared to the Ly6Chi CD4+ T cells. We show that GITR had a similar effect on the accumulation of both the Ly6Chi and Ly6Clo CD4+ T cell subsets. In contrast, whereas GITR increased the accumulation of all three CD8+ T cell subsets defined by CX3CR1 and Ly6C expression, it had a more substantial effect on the least differentiated Ly6Clo CX3CR1lo subset. Moreover, GITR selectively up-regulated CXCR6 on the less differentiated CX3CR1lo CD8+ T cell subsets and induced a small but significant increase in CD127 selectively on the Ly6Clo CD4+ T cell subset. Thus, GITR contributes to accumulation of both differentiated effector cells as well as memory precursors, but with some differences between subsets.
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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".