Estrogen-independent actions of environmentally relevant AhR-agonists in human endometrial epithelial cells
Bibliographic record
Abstract
The human endometrium is a cyclically regenerating organ under the influence of ovarian steroid hormones. Disturbances in this highly coordinated regulation of endometrial proliferation and differentiation may result in infertility and diseases such as endometriosis and endometrial cancer. Environmental toxins belonging to the group of polyhalogenated aromatic hydrocarbons (PAHs) are lipophilic xenobiotics, which accumulate in biological systems. PAHs have been implicated in the etiology of uterine pathologies, including infertility, endometriosis and endometrial cancer. However, suitable cellular models of the endometrium are lacking and the molecular mechanism of PAH action in the endometrium is not fully understood. In this study, we have characterized a previously established immortalized human telomerase reverse transcriptase (hTERT) endometrial epithelial cell (hTERT-EEC) model as a responsive in vitro cell model to investigate the cellular and molecular mechanisms of selected environmentally relevant PAH in human EECs. We show that dioxin-type PAHs activate the endogenous arylhydrocarbon receptor (AhR) signaling pathway in hTERT-EEC in a time-, concentration- and congener-specific manner and that the induction of AhR target genes is modulated by estrogen. Strikingly, AhR activation did not interfere with estrogenic actions in these EECs. Independent of their ability to bind to AhR, the PAHs investigated here increased cell migration by hTERT-EEC. Furthermore, we have identified several candidates by proteomic analysis, which are involved in heat shock responses and protein modification and turnover. Our data suggest that AhR-activating environmental pollutants directly alter endometrial cell stress responses and metabolism independent of estrogenic actions.
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".