Expression and role of VLA-1 in resident memory CD8 T cell responses to respiratory mucosal viral-vectored immunization against tuberculosis
Bibliographic record
Abstract
Abstract Lung resident memory T cells (TRM) characterized by selective expression of mucosal integrins VLA-1 (α1β1) and CD103 (αEβ7) are generated following primary respiratory viral infections. Despite recent progress, the generation of lung TRM and the role of mucosal integrins following viral vector respiratory mucosal immunization still remains poorly understood. Here by using a replication-defective viral vector tuberculosis vaccine, we show that lung Ag-specific CD8 T cells express both VLA-1 and CD103 following respiratory mucosal immunization. However, VLA-1 and CD103 are acquired in differential tissue sites with the former acquired during T cell priming in the draining lymph nodes and the latter acquired after T cells entered the lung. Once in the lung, Ag-specific CD8 T cells continue to express VLA-1 at high levels through the effector/expansion, contraction, and memory phases of T cell responses. Using a functional VLA-1 blocking mAb, we show that VLA-1 is not required for trafficking of these cells to the lung, but it negatively regulates them in the contraction phase. Furthermore, VLA-1 plays a negligible role in the maintenance of these cells in the lung. Our study provides new information on vaccine-inducible lung TRM and shall help develop effective viral vector respiratory mucosal tuberculosis vaccination strategies.
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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".