Impact of Exposure to a Mixture of Organophosphate Esters on Adrenal Cell Phenotype, Lipidome, and Function
Bibliographic record
Abstract
Organophosphate esters (OPEs) are used primarily as flame retardants and plasticizers. Previously, we reported that adrenal cells are important targets of individual OPEs. However, real-life exposures are to complex mixtures of these chemicals. To address this, we exposed H295R human adrenal cells to varying dilutions (1/1000K to 1/3K) of a Canadian household dust-based OPE mixture for 48 hours and evaluated effects on phenotypic, lipidomic, and functional parameters. Using a high-content screening approach, we assessed phenotypic markers at mixture concentrations at which there was greater than 70% cell survival; the most striking effect of the OPE mixture was a 2.5-fold increase in the total area of lipid droplets. We then determined the response of specific lipid species to OPE exposures with novel, nontargeted lipidomic analysis of isolated lipid droplets. These data revealed that house dust OPEs induced concentration-dependent alterations in the composition of lipid droplets, particularly affecting the triglyceride, diglyceride, phosphatidylcholine, and cholesterol ester subclasses. The steroid-producing function of adrenal cells in the presence or absence of a steroidogenic stimulus, forskolin, was determined. While the production of 17β-estradiol remained unaffected, a slight decrease in testosterone production was observed after stimulation. Conversely, a 2-fold increase in both basal and stimulated cortisol and aldosterone production was observed. Thus, exposure to a house dust-based mixture of OPEs exerts endocrine-disrupting effects on adrenal cells, highlighting the importance of assessing the effects of environmentally relevant mixtures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".