Transcriptomic analysis of BPA alternative chemicals in primary human mammary epithelial cells
Bibliographic record
Abstract
Some everyday consumer products contain endocrine disruptors like bisphenol A (BPA) and its replacements. To date, most in vitro chemical screening to evaluate these compounds has been accomplished using immortalized cell lines, which differ significantly from human tissues. Our goal was to test BPA and select alternatives previously screened in breast cancer cells for toxicological potency and mechanism of action in human mammary epithelial cells (HMECs). HMECs from three human donors were exposed to BPA and four alternative chemicals (in concentration response format from 0.001 to 50 µM) for 48 h and global transcriptomic changes were quantified. Transcriptomic biomarker analysis was employed to explore chemically induced estrogen receptor alpha (ERα) activation and alterations in stress response pathways. Benchmark concentration (BMC) analysis was applied to gene expression data to derive transcriptomic points of departure (tPODs) to compare chemicals for potency. Pathway and upstream regulator analysis was applied among the genes fitting BMCs. All chemicals had tPODs within a single order of magnitude. Bisphenol AF (BPAF) was the most potent, followed by tetramethyl bisphenol F (TMBPF), bisphenol C (BPC), 4,4'-bisphenol S (BPS), and BPA. None of the chemicals activated the ERα biomarker. Some stress response biomarkers were activated at high exposure concentrations. Genes fitting BMCs clustered chemicals into two groups, with one group (BPAF and TMBPF) primarily inhibiting expression patterns and the other (BPC, BPS, and BPA) mostly activating. These data suggest that the BPA alternatives tested have similar toxicological potencies in HMECs and oppositely enrich various gene sets.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".