Long-term effects of wildfire smoke exposure during early life on the nasal epigenome in rhesus macaques
Bibliographic record
Abstract
Wildfire smoke is responsible for around 20% of all particulate emissions in the U.S. and affects millions of people worldwide. Children are especially vulnerable, as ambient air pollution exposure during early childhood is associated with reduced lung function. Most studies, however, have focused on the short-term impacts of wildfire smoke exposures. We aimed to identify long-term baseline epigenetic changes associated with early-life exposure to wildfire smoke. We collected nasal epithelium samples for whole genome bisulfite sequencing (WGBS) from two groups of adult female rhesus macaques: one group born just before the 2008 California wildfire season and exposed to wildfire smoke during early-life (n = 8), and the other group born in 2009 with no wildfire smoke exposure during early-life (n = 14). RNA-sequencing was also performed on a subset of these samples. We identified 3370 differentially methylated regions (DMRs) (difference in methylation ≥ 5%, empirical p < 0.05) and 1 differentially expressed gene (FLOT2) (FDR < 0.05, fold of change ≥ 1.2). The DMRs were annotated to genes significantly enriched for synaptogenesis signaling, protein kinase A signaling, and a variety of immune processes, and some DMRs significantly correlated with gene expression differences. DMRs were also significantly enriched within regions of bivalent chromatin (top odds ratio = 1.46, q-value < 3 × 10-6) that often silence key developmental genes while keeping them poised for activation in pluripotent cells. These data suggest that early-life exposure to wildfire smoke leads to long-term changes in the methylome over genes impacting the nervous and immune systems. Follow-up studies will be required to test whether these changes influence transcription following an immune/respiratory challenge.
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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 | 0.000 | 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 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".