Transcriptomic Response of Airway Epithelium to Diesel Exhaust and Woodsmoke
Bibliographic record
Abstract
Abstract Background: The airway epithelium plays a crucial role as a mucosal barrier against environmental challenges, including traffic-related air pollution and wildfire smoke – the two most common exposures in North America. By utilizing an in-vitro exposure cell culture model and transcriptomic analysis to assess the effects of diesel exhaust (DE; a model for traffic-related air pollution) and wood smoke (WS; a model for wildfires), we can gain a comprehensive understanding of the similarities and differences in how each exposure impacts the respiratory tract. Methods: Human bronchial epithelial cells (hBEC) were collected from six healthy never-smokers undergoing a research bronchoscopy. Cells were cultured, expanded, and differentiated at air-liquid interface (ALI) for >21 days. Differentiated hBECs were exposed to filtered air (control condition), diesel exhaust (diluted to PM2.5 = 300 μg/m3), or woodsmoke (diluted to PM2.5 = 300 μg/m3) for 2 hours using a CULTEX in-vitro exposure system. Twenty-four hours after each exposure, the cells were harvested for RNA sequencing. Total RNA was extracted, followed by PolyA mRNA enrichment, cDNA synthesis, and sequencing library generation. Paired-end 150bp Illumina NovaSeq sequencing targeting 50 million read-pairs per library was then performed. After preprocessing, we conducted differential gene expression and pathway analysis through the nf-core RNA-seq pipeline before conducting DESeq2 differential expression analysis and Gene Set Enrichment Analysis (GSEA). Results: Transcriptome analysis revealed 283 (Up:162; Down:121) differentially expressed genes (DEGs) in DE-exposed cells compared to FA exposure (false discovery rate (FDR) <0.05). In contrast, there were 83 (Up:24; Down:59) DEGs in WS-exposed cells compared to control. Both DE and WS exposures elicited changes in 18 common differentially expressed transcripts (Up:5; Down:13). GSEA analysis showed enrichment of 33 and 12 KEGG Pathways in the DE and WS exposed cells, respectively, based on DEGs. GSEA further revealed shared enriched pathways (including oxidative phosphorylation and chemical carcinogenesis) between the DE and WS-exposed cells. No significant differences in cytotoxicity (LDH assay) or barrier function (transepithelial electrical resistance) were observed when comparing the FA condition to the exposure groups. Conclusions: This study demonstrates the utility of a systems biology approach in uncovering distinct and shared transcriptional responses of bronchial epithelial cells to two extremely common environmental exposures. Given some public perception of WS as inconsequential, along with alternative assumptions that similar concentrations of DE and WS likely have indistinct effects, these findings highlight the importance of understanding the intricacies of complex pollution-induced changes in airway health.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".