Lung epithelial signaling mediates early vaccine-induced CD4 <sup>+</sup> T cell activation and <i>Mtb</i> control
Bibliographic record
Abstract
Abstract Tuberculosis (TB) is one of the leading causes of death due to a single infectious agent. The development of a TB vaccine that induces durable and effective immunity to Mycobacterium tuberculosis ( Mtb ) infection is urgently needed. Early and superior Mtb control can be induced in M. bovis Bacillus Calmette–Guérin (BCG) vaccinated hosts when the innate immune response is targeted to generate effective vaccine-induced immunity. In the present study, we show that innate activation of DCs is critical for mucosal localization of clonally activated vaccine-induced CD4 + T cells in the lung, and superior early Mtb control. In addition, our study reveals that Th1/Th17 cytokine axis play an important role in superior vaccine induced immunity. Our studies also show that activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NFκβ) pathway in lung epithelial cells is critical for the mucosal localization of activated vaccine-induced CD4 + T cells for rapid Mtb control. Thus, our study provides novel insights into the immune mechanisms that can overcome TB vaccine bottlenecks and provide early rapid Mtb control. Importance Tuberculosis is a leading cause of death due to single infectious agent accounting 1.4 million deaths each year. The only licensed vaccine BCG is not effective due to variable efficacy. In our study, we determined the early immune events necessary for achieving complete protection in BCG vaccinated host. Our study reveals that innate activation of DCs can mediate superior and early Mtb control in BCG vaccinated host through lung epithelial cell signaling and localization of clonal activated, Mtb antigen specific, cytokine producing CD4 + T cells within the lung parenchyma and airways. Thus, our study provides novel insights into the immune mechanisms that can overcome TB vaccine bottlenecks and provide early rapid Mtb control.
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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) | 0.003 | 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".