Faculty Opinions recommendation of BCG Educates Hematopoietic Stem Cells to Generate Protective Innate Immunity against Tuberculosis.
Bibliographic record
Abstract
The dogma that adaptive immunity is the only arm of the immune response with memory capacity has been recently challenged by several studies demonstrating evidence for memory-like innate immune training. However, the underlying mechanisms and location for generating such innate memory responses in vivo remain unknown. Here, we show that access of Bacillus Calmette-Guérin (BCG) to the bone marrow (BM) changes the transcriptional landscape of hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs), leading to local cell expansion and enhanced myelopoiesis at the expense of lymphopoiesis. Importantly, BCG-educated HSCs generate epigenetically modified macrophages that provide significantly better protection against virulent M. tuberculosis infection than naïve macrophages. By using parabiotic and chimeric mice, as well as adoptive transfer approaches, we demonstrate that training of the monocyte/macrophage lineage via BCG-induced HSC reprogramming is sustainable in vivo. Our results indicate that targeting the HSC compartment provides a novel approach for vaccine development.Copyright © 2017 Elsevier Inc. All rights reserved. PMID: 29328912 Funding information This work was supported by: CIHR, Canada Grant ID: 232519 CIHR, Canada Grant ID: 301538 CIHR, Canada Grant ID: FDN-143273
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.082 | 0.041 |
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".