BCG vaccination impacts the epigenetic landscape of progenitor cells in human bone marrow
Bibliographic record
Abstract
While the Bacille-Calmette-Guérin (BCG) vaccine is used to prevent tuberculosis, it also offers protection against a diverse range of non-mycobacterial infections. However, the underlying protective mechanisms in humans are not yet fully understood. Here, we surveyed at single-cell resolution the gene expression and chromatin landscape of human bone marrow, aspirated before and 90 days after BCG vaccination or placebo administration. We show that BCG vaccination significantly alters both the gene expression and epigenetic profiles of human hematopoietic stem and progenitor cells (HSPCs). Changes in gene expression occur primarily on the most uncommitted stem cells and are reflective of a persistent myeloid bias. In contrast, BCG-induced changes in chromatin accessibility are most prevalent within differentiated progenitor cells at sites influenced by Kruppel-like factor (KLF)/SP and EGR transcription factors (TFs). These TFs are also activated in the most uncommitted stem cells, indicating that activated TFs, which drive persistent changes in HSC gene expression, likely also drive chromatin dynamics appearing within downstream progenitor cells. This perspective contests the prevailing notion that epigenetic modifications linked to innate immune memory transfer directly from stem cells to their differentiated derivatives. Finally, we show that alterations in gene expression and chromatin accessibility in HSPCs due to BCG vaccination were highly correlated (r>0.8) with the IL-1β secretion capacity of paired PBMCs upon secondary immune challenge. Overall, our findings shed light on BCG vaccination's profound and lasting effects on HSPCs and its influence on innate immune responses.
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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 | 0.000 |
| 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".