Controlled human exposures to diesel exhaust or particle-depleted diesel exhaust with allergen modulates transcriptomic responses in the lung
Bibliographic record
Abstract
The evidence associating traffic-related air pollution (TRAP) with allergic asthma is growing, but the underlying mechanisms for this association remain unclear. The airway epithelium is the primary tissue exposed to TRAP, hence understanding its interactions with TRAP and allergen is important. Diesel exhaust (DE), a paradigm of TRAP, consists of particulate matter (PM) and gases. Modern diesel engines often have catalytic diesel particulate filters to reduce PM output, but these may increase gaseous concentrations, and their benefits on human health cannot be assumed. We conducted a randomized, double-blinded, crossover study using our unique in vivo human exposure system to investigate the effects of DE and allergen co-exposure, with or without particle depletion as a proxy for catalytic diesel particulate filters, on the airway epithelial transcriptome. Participants were exposed for 2 h before an allergen inhalation challenge, with each receiving filtered air and saline (FA-S), filtered air and allergen (FA-A), DE and allergen (DE-A), or particle-depleted DE and allergen (PDDE-A), over four different occasions, each separated by a 4-week washout period. Endobronchial brushings were collected 48 h after each exposure, and total RNA was sequenced. Differentially expressed genes (DEGs) were identified using DESeq2, followed by GO enrichment and pathway analysis. FA-A, DE-A, and PDDE-A exposures significantly modulated genes relative to FA-S, with 462 unique DEGs identified. FA-A uniquely modulated the highest number (↑178, ↓155), followed by DE-A (↑44, ↓23), and then PDDE-A exposure (↑15, ↓2); 6 DEGs (↑4, ↓2) were modulated by all three conditions. Exposure to PDDE-A resulted in modulation of 285 DEGs compared to DE-A exposure, further revealing 26 biological process GO terms, including "cellular response to chemokine" and "inflammatory response". The transcriptional epithelial response to diesel exhaust and allergen co-exposure is enriched in inflammatory mediators, the pattern of which is altered upon particle depletion.
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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) | 0.001 | 0.001 |
| 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) | 0.003 | 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".