Transplacental innate immune training via maternal microbial exposure: the XBP1-ERN1 axis in programming dendritic cell precursors
Bibliographic record
Abstract
Abstract We recently reported that the offspring of mice treated during pregnancy with the microbial-derived immunomodulator OM-85 manifest striking resistance postnatally to allergic airways inflammation, and localised the potential treatment target to the fetal cDC progenitor compartment which expands to increase the pool of precursors available at birth, enabling accelerated postnatal seeding of the lung mucosal cDC network required for establishment of immunological homeostasis in the airways. Here, we profile maternal OM-85 treatment-associated transcriptomic signatures in fetal bone marrow, and identify a series of immunometabolic pathways which provide essential metabolites for accelerated myelopoiesis, that are hallmarks of classical “immune training”. In addition, the cDC progenitor compartment displayed treatment-associated activation of the XBP1-ERN1 signalling axis which has previously been shown to be essential for tissue survival of cDC, particularly within the lung microenvironment. Our forerunner studies indicate uniquely rapid turnover of airway mucosal cDCs at baseline, with further large-scale upregulation of population dynamics during aeroallergen and/or pathogen challenge. XBP1-ERN1 signalling plays a key role in mitigation of ER stress-associated toxicity which frequently accompanies DC hyper-activation during intense immunoinflammatory responses, and we suggest that enhanced capacity for XBP1-ERN1-dependent cDC survival within the airway mucosal tissue microenvironment may be a crucial element of the OM-85-mediated transplacental “innate immune training” process which results in enhanced resistance to airway inflammatory disease during the high-risk early postnatal period.
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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".