Controlled Human Exposure to Diesel Exhaust and Particle-Depleted Diesel Exhaust with Allergen Modulates Transcriptomic Responses in the Lung Epithelium
Bibliographic record
Abstract
RATIONALE: While allergen exposure has been a primary focus of asthma development and exacerbation research, the evidence associating traffic-related air pollution (TRAP) with asthma is growing. Diesel exhaust (DE) is a paradigm of TRAP, a complex mixture of particulate matter (PM) and gases, and although modern diesel engines include catalytic diesel particulate filters (cDPF) to reduce PM output, such systems may increase gas production, and their effects on health remain unclear. To investigate DE-associated augmentation of allergen effects in the context of PM reduction, we conducted a controlled human exposure study with allergen inhalation, DE, and particle-depleted DE exposures, and investigated transcriptomic responses in the lung epithelium. HYPOTHESIS: In the presence of allergens, the removal of particles from DE decreases inflammationrelated gene expression in the lung epithelium. METHODS: We conducted a randomized, double-blinded, crossover study using our unique in vivo human exposure system. High-efficiency particulate air filters and electrostatic precipitation were used to simulate cDPF, decreasing PM 2.5 by 94% but increasing NO 2 by 350%. Participants were exposed for 2h before undergoing an allergen inhalation challenge, with each participant receiving either filtered air (FA) and saline (FA-S), FA and allergen (FA-A), DE and allergen (DE-A), or particulatedepleted DE and allergen (PDDE-A) on 4 different occasions separated by 4 weeks. Endobronchial brushings (EBs) were collected 48h after exposures. Total RNA was extracted and sequenced using an Illumina NovaSeq platform. Quality was checked using FastQC, followed by alignment and quantification against transcriptome Ensembl-v98 using Salmon. Differentially expressed genes (DEGs) were identified using DESeq2 (FDR-adjusted pvalue < 0.1) followed by GO enrichment analysis using g:Profiler. RESULTS: FA-A, DE-A, and PDDE-A exposures significantly modulated gene expression relative to control (FA-S) in EBs. Therein, 747 DEGs were identified, with 8 (5, 3) modulated by all three conditions; FA-A modulated the most genes (326, 282), followed by DE-A (90, 68), and then PDDE-A (44, 13). PDDE-A modulated 557 genes (299, 258) compared to DE-A, including many that may participate in inflammatory responses (eg: increased MMP12, which contributes to airway remodeling via extracellular matrix degradation; suppressed IL22RA2, which inhibits IL-22 activity in inflammatory response activation) and enrichment analysis highlighted chemokine-mediated signaling pathways. CONCLUSIONS: The transcriptome was significantly modulated following acute exposure to FA-A, DE-A, and PDDE-A, relative to FA-S in EBs. Removing PM from DE does not necessarily eliminate inflammation-related gene expression. These results contribute to an improved understanding of the effects of PM exposure in asthma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".