Mice exposed in situ to urban air pollution exhibit pulmonary alterations in gene expression in the lipid droplet synthesis pathways
Bibliographic record
Abstract
It is clear that particulate air pollution poses a serious risk to human health; however, the underlying mechanisms are not completely understood. We investigated pulmonary transcriptional responses in mice following in-situ exposure to ambient air in a heavily industrialized urban environment. Mature C57BL/CBA male mice were caged in sheds near two working steel mills and a major highway in Hamilton, Ontario, Canada in the spring/summer of 2004. Control mice were housed in the same environment, but received only high-efficiency particle filtered air (HEPA). Whole lung tissues were collected from mice exposed for 3, 10, or for 10 weeks followed by 6 weeks recovery in the laboratory (16 weeks). DNA microarrays were used to profile changes in pulmonary gene expression. Transcriptional profiling revealed changes in the expression of genes implicated in the lipid droplet synthesis (Plin I, Dgat2, Lpl, S3-12, and Agpat2), and antioxidant defense (Ucp1) pathways in mice breathing unfiltered air. We postulate that exposure to urban air, containing an abundance of particulate matter adsorbed with polycyclic aromatic hydrocarbons, triggers lipid droplet (holding depots for lipids and malformed/excess proteins tagged for degradation) synthesis in the lungs, which may act to sequester particulates. Increased lipid droplet synthesis could lead to endogenous/stressor-induced production of reactive oxygen species and activation of antioxidant mechanisms. Further investigation into the stimulation of lipid droplet synthesis in the lung in response to air pollution and the resulting health implications is warranted.
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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 | 0.001 | 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) | 0.001 | 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".