Systemic BCG Immunization Induces Persistent Lung Mucosal Multifunctional CD4 TEM Cells which Expand Following Virulent Mycobacterial Challenge
Bibliographic record
Abstract
To more closely understand the mechanisms of how BCG vaccination confers immunity would help to rationally design improved tuberculosis vaccines that are urgently required. Given the established central role of CD4 T cells in BCG induced immunity, we sought to characterise the generation of memory CD4 T cell responses to BCG vaccination and M. bovis infection in a murine challenge model. We demonstrate that a single systemic BCG vaccination induces distinct systemic and mucosal populations of T effector memory (T(EM)) cells in vaccinated mice. These CD4+CD44(hi)CD62L(lo)CD27⁻ T cells concomitantly produce IFN-γ and TNF-α, or IFN-γ, IL-2 and TNF-α and have a higher cytokine median fluorescence intensity MFI or 'quality of response' than single cytokine producing cells. These cells are maintained for long periods (>16 months) in BCG protected mice, maintaining a vaccine-specific functionality. Following virulent mycobacterial challenge, these cells underwent significant expansion in the lungs and are, therefore, strongly associated with protection against M. bovis challenge. Our data demonstrate that a persistent mucosal population of T(EM) cells can be induced by parenteral immunization, a feature only previously associated with mucosal immunization routes; and that these multifunctional T(EM) cells are strongly associated with protection. We propose that these cells mediate protective immunity, and that vaccines designed to increase the number of relevant antigen-specific T(EM) in the lung may represent a new generation of TB vaccines.
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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".