Early life exposure to cigarette smoke primes lung response to later life exposure in a mouse model
Bibliographic record
Abstract
Cigarette smoke (CS) exposure in early life causes long-lasting lung function defects even years after the exposure itself. Long term regulation of genes by epigenetic marks may be one of the mechanisms maintaining the cellular memory of early life exposure and influencing the response to additional exposures later in life. Here, we investigated the effects of early life CS exposure on mouse lung epigenetic marks across time, and how those marks were altered by re-exposure later in life. We found that early life exposure to CS induced a differential susceptibility to later life exposure – when exposed to CS in adulthood, animals with early life exposure had higher immune cell infiltration in the lung compared to those without early life exposure. Whole genome DNAm and ATACseq data show that epigenetic marks associated with early life CS exposure in the lung wane over time, but are re-established upon re-exposure at over 100 genes, including important epigenetic remodelers and immune related genes. Our results show that CS exposure in early life primes the cellular response to later life exposure, which may occur through modifications to the epigenome. DNA methylation is unlikely to be the main regulator, as DNAm changes are not maintained to adulthood in the absence of re-exposure. Future work examining specific transcription factors and histone modifications will be needed in order to design interventions to break the molecular links between early life CS and long lasting alterations to lung function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".