Woodsmoke and Diesel Exhaust: Distinct Transcriptomic Profiles in the Human Airway Epithelium
Bibliographic record
Abstract
ABSTRACT Climate change is increasing the frequency and severity of wildfires globally, causing significant woodsmoke (WS) emissions. Vehicles emit sizable amounts of toxic traffic-related air pollution (TRAP), for which diesel exhaust (DE) is a model. Both WS and DE contain particulate matter <2.5 microns (PM 2.5 ) which deeply penetrates the lungs, causing respiratory epithelial inflammation that drives health effects. Regulations focus on PM 2.5 concentration, despite emerging research that highlights how composition mediates health effects. As WS and DE are compositionally distinct, we conducted the first head-to-head comparison of effects on the transcriptomes of air-liquid interface cultured primary human bronchial epithelial cells (HBEC). Differentiated donor-matched HBEC transwells were exposed for 2-hours to filtered air (FA; control), or WS (furnace tube burning pine) or DE (Hatz 1B30E generator) both diluted to 300 µg/m 3 of PM 2.5 . WS had higher ultrafine PM, whereas DE exposure contained significantly higher NO 2 , CO, and O 3 . RNA sequencing showed that WS exposure resulted in 119 (↑41, ↓78) differentially expressed genes, while DE modulated 399 (↑255, ↓144) compared to FA exposure. WS was associated with small ribosomal subunit and cytochrome complex related genes, while DE exposure was associated with HIF-1 signaling, respiratory chain complex and interferon alpha/beta signaling/ISG15-protein conjugation, suggesting how TRAP exposure may enhance infection risk. We also analyzed exposure effects on protein immune-mediators. We demonstrate that two major air pollution sources modulate different genes and pathways in HBECs, with minimal overlap. This informs the debate regarding the regulatory focus on concentration and assumptions that similar concentrations of air pollution have indistinct effects. Highlights Lung health effects of diesel exhaust (DE) and wood smoke (WS) are underexplored RNAseq of DE and WS-exposed primary lung epithelial cells revealed larger DE effects DE showed greater repression of host antiviral response-associated genes than WS A regulatory focus on PM concentration may miss composition-specific lung effects
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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) | 0.002 | 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".