An Environmentally Relevant Mixture of Organophosphate Esters Induces Cholesterol Biosynthesis in Thp-1 Macrophages
Bibliographic record
Abstract
Organophosphate esters (OPEs), widely used as flame retardants and plasticizers, are environmental toxicants known to disrupt lipid metabolism. Although most studies have focused on individual OPEs, environmental exposures typically involve complex mixtures. Our previous studies demonstrated that a representative OPE mixture from Canadian household dust promotes cholesterol and lipid droplet accumulation in THP-1 macrophages. However, the molecular mechanisms underlying this lipid dysregulation remain unclear. Here, we employed tandem mass tag (TMT)-based quantitative proteomics to investigate how OPE mixtures alter protein expression and lipid regulation in macrophages. THP-1 macrophages were exposed to vehicle or environmentally relevant dilutions of the OPE mixture for 48 h. Lysates were subjected to TMT labeling and mass spectrometry. Bioinformatic analyses using STRING and Ingenuity Pathway Analysis identified 162 differentially expressed proteins, with unsupervised clustering highlighting cholesterol biosynthesis as a key pathway. Further validation via qPCR and upstream analysis implicated the sterol regulatory element-binding protein 2 (SREBP2) signaling axis in OPE-induced cholesterol biosynthesis. Functional assays revealed that atorvastatin-mediated HMG-CoA reductase inhibition, the rate limiting enzyme in cholesterol biosynthesis, prevents cholesterol buildup and lipid droplet formation in macrophages. These findings provide the first evidence that an environmentally relevant OPE mixture can induce cholesterol biosynthesis in human macrophages. These studies provide mechanistic evidence that an environmentally relevant mixture of organophosphate esters induces cholesterol biosynthesis in macrophages. These findings link real-world exposure to lipid pathways implicated in metabolic disease and support the need for updated regulatory standards that protect human health.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".