The effects of house dust-derived mixtures of organophosphate esters on Leydig cell phenotype, function, and lipidome
Bibliographic record
Abstract
Organophosphate esters (OPEs), widely used as flame retardants and plasticizers, are frequently detected in indoor environments and human tissues, raising concerns about their potential endocrine-disrupting effects. In this study, we examined the effects of a household dust-based mixture of OPEs, along with two structural distinct sub-mixtures, on the phenotype, function, and lipidome on MA10 Leydig cells. Using high-content imaging, we identified increase in oxidative stress levels and accumulation of lipid droplets as common phenotypic effects across mixtures. Notably, the triaryl OPE sub-mixture exhibited greater potency, suggesting that specific structural features contribute to the toxicity of OPEs. While the OPE mixture did not impair basal steroid hormone production in MA-10 cells, changes were observed in stimulated progesterone levels and transcriptional regulation of key steroidogenic transcripts. When comparing lipidomic profiles across three steroidogenic cell lines (MA-10, H295R, and KGN), we found that glycerolipids, particularly triglycerides and diglycerides, consistently appeared to be the most affected lipid species, highlighting a common disruption in the composition of lipid droplet. However, cell line specific effects were also observed, especially in the regulation of cholesterol esters, likely reflecting differences in cholesterol sourcing and steroidogenic pathways. These findings emphasize the importance of evaluating environmentally relevant chemical mixtures and demonstrate that OPEs can disrupt steroidogenic function and lipid metabolism.
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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) | 0.001 | 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".