Nepalese indoor cookstove smoke extracts alter human airway epithelial gene expression, DNA methylation and hydroxymethylation
Bibliographic record
Abstract
Household air pollution caused by inefficient cooking practices causes 4 million deaths a year worldwide. In Nepal, 86% of the rural population use solid fuels for cooking. Over 25% of premature deaths associated with air pollution are respiratory in nature. Here we aimed to identify molecular signatures of different cookstove and fuel type exposures in human airway epithelial cells, to understand the mechanisms mediating cook stove smoke induced lung disease. Primary human airway epithelial cells in submerged culture were exposed to traditional cook stove (TCS), improved cook stove (ICS) and liquefied petroleum gas (LPG) stove smoke extracts. Changes to gene expression, DNA methylation and hydroxymethylation were measured by bulk RNA sequencing and HumanMethylationEPIC BeadChip following oxidative bisulphite conversion, respectively. TCS smoke extract alone reproducibly caused changes in the expression of 52 genes enriched for oxidative stress pathways. TCS, ICS and LPG smoke extract exposures were associated with distinct changes to DNA methylation and hydroxymethylation. A subset of TCS induced genes were associated with differentially methylated and/or hydroxymethylated CpGs sites, and enriched for the ferroptosis pathway and the upstream regulator NFE2L2. DNA methylation and hydroxymethylation changes not associated with a concurrent change in gene expression, were linked to biological processes and molecular pathways important to airway health, including neutrophil function, transforming growth factor beta signalling, GTPase activity, and cell junction organisation. Our data identified differential impacts of TCS, ICS and LPG cook stove smoke on the human airway epithelium transcriptome, DNA methylome and hydroxymethylome and provide further insight into the association between indoor air pollution exposure and chronic lung disease mechanisms.
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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".