High-throughput transcriptomics analysis of equipotent and human relevant mixtures of BPA alternatives reveal additive effects in vitro
Bibliographic record
Abstract
While many jurisdictions have phased out use of bisphenol A (BPA), there is increasing exposure to mixtures of BPA alternatives. Like BPA, some alternatives perturb nuclear hormone receptors and are endocrine disruptors. We used high-throughput transcriptomics (HTTr) to evaluate the potency and modes of action of seven mixtures of BPA alternatives and their 12 individual components in breast cancer cells. Our aim was to explore whether alternatives present in mixtures act additively. MCF-7 cells were exposed to chemicals (0.001-50 µM) for 48 h and gene expression analysis was used to measure global and estrogen receptor alpha (ERα)-specific transcriptomic changes. Transcriptomic points of departure (tPODs) were derived using benchmark concentration (BMC) modelling. We first identified concentrations at which global transcriptional activity was robustly altered. Then, we applied a ERα transcriptomic biomarker to identify ERα agonists and predict ERα activation tPODs. We employed mixtures modeling to predict potency of BPA alternatives and test for additive effects in vitro. Ingenuity pathway analysis (IPA; Qiagen) was used to identify upstream regulators and canonical pathways from genes fitting BMCs. BPAF was the most potent individual chemical tested overall, followed by BPA and BPC. All seven mixtures had additive effects across all tPODs modeled. The ERα transcriptomic biomarker classified all mixtures as ERα activators along with several BPA alternatives. All mixtures and most individual components perturbed similar upstream regulators and pathways, suggesting common modes of action. These data support the value of HTTr in identifying additive effects and toxicological potency of mixtures in vitro.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".