Mucosal-Pull Induction of Lung-Resident Memory CD8 T Cells in Parenteral TB Vaccine-Primed Hosts Requires Cognate Antigens and CD4 T Cells
Bibliographic record
Abstract
Tissue resident memory T cells (TRM) are critical to host defense at mucosal tissue sites. However, the parenteral route of immunization as the most commonly used immunization route in practice is not effective in inducing mucosal TRM cells particularly in the lung. While various respiratory mucosal (RM)-pull strategies are exploited to mobilize parenteral vaccine-primed T cells into the lung, whether such RM pull strategies can all or differentially induce Ag-specific TRM cells in the lung remains unclear. Here we have addressed this issue by using a parenteral TB vaccine-prime and RM-pull model. We show that both Ag-independent and Ag-dependent RM pull strategies are able to mobilize Ag-specific CD8 T cells into the lung. However, only the RM pull strategy with cognate antigens can induce robust Ag-specific CD8 TRM cells in the lung. We also show that the cognate Ag-based RM pull strategy is the most effective in inducing TRM cells when carried out during the memory phase, as opposed to the effector phase, of T cell responses to parenteral prime vaccination. We further find that cognate Ag-induced CD4 T cells play an important role in the development of CD8 TRM cells in the lung. Our study holds implications in developing effective vaccine strategies against respiratory pathogens.
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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 | 0.001 |
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".