Systemic BCG Vaccination in “Dirty Mice” induces Protective Trained Immunity against TB
Bibliographic record
Abstract
Abstract After a century of discovery of Bacille Calmette-Guerin (BCG), our understanding of its protective mechanism(s) against TB or other pathogens is very limited. We have recently demonstrated that systemic BCG vaccination (intravenously, iv) in specific pathogen-free (SPF) mice imprints hematopoietic stem cells (HSCs) to generate macrophages with a unique protective program against pulmonary M. tuberculosis (Mtb) infection. While this proof of concept study was an important step to determine how we can harness the power of innate (trained) immunity in vaccination against TB, the translation of this novel approach to humans is still unknown. As SPF lab mice incompletely recapitulate the human immune system, additional pre-clinical models are necessary to evaluate the translational potential of systemic BCG vaccination. Thus, in the current study, we hypothesize that systemic BCG vaccination will generate protective HSC-mediated trained immunity against TB in microbe-exposed “dirty” mice whose immune landscape more closely replicates adult human traits. To test this hypothesis, we have established a bedding transfer model from pet shop mice to SPF C57BL/6 mice. Age- and sex-matched dirty or SPF mice were then iv or subcutaneously vaccinated with BCG (1×106 CFU) or PBS (control). At 4 weeks post vaccination, we generated bone marrow derived macrophages and infected them with virulent Mtb (H37Rv; MOI 1). Interestingly, the protective signature of trained immunity was completely intact in BCG-iv vaccinated dirty mice. Collectively, our results indicate that the protective imprinting of systemic BCG vaccination for generation of trained immunity is robust and independent of previous exposure of hosts to other microbes or pathogens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".