Ozone increases plasma kynurenine-tryptophan ratio and impacts hippocampal serotonin receptor and neurotrophic factor expression: Role of stress hormones
Bibliographic record
Abstract
Air pollution is associated with adverse impacts on the brain, including cognitive decline and increased incidence of dementia, depression and anxiety; however, underlying mechanisms remain unclear. We have shown that both ozone and particulate matter activate the hypothalamic-pituitary-adrenal (HPA) axis, increasing plasma glucocorticoids and altering mRNA profiles in multiple tissues including the brain. HPA axis dysregulation has been associated with central nervous system impacts, including key effects in the hippocampus; accordingly, we hypothesized that pollutant-dependent increases in glucocorticoid levels impact biological pathways relevant to brain health. Fischer-344 rats were treated with metyrapone (0 or 50 mg/kg), a glucocorticoid synthesis inhibitor, and exposed to ozone (0 or 0.8 ppm) for 4 h (n = 5/group) to investigate the role of glucocorticoids in ozone-dependent effects on tryptophan metabolism and expression of serotonin receptors and neurotrophic factors. Ozone increased plasma levels of the tryptophan metabolite kynurenine (~2-fold) and decreased tryptophan levels (~1.2 fold). Hippocampal expression of serotonin receptors exhibited differential regulation following exposure, and expression of key neurotrophic factors (brain-derived neurotrophic factor, vascular endothelial growth factor A, insulin-like growth factor-1, tyrosine kinase receptor B, b-cell lymphoma 2) was decreased. Some, but not all effects were abrogated by metyrapone treatment, suggesting both glucocorticoid-dependent and -independent regulation. Exposure to exogenous corticosterone (10 mg/kg) followed by clean air reproduced the ozone effects that were blocked with metyrapone, confirming the specificity of effects to glucocorticoids. These results indicate that ozone can modify pathways relevant to brain health and establish a role for the HPA axis in mediating these effects.
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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 | 0.001 |
| 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".