Antiandrogenic Mechanisms of Pesticides in Human LNCaP Prostate and H295R Adrenocortical Carcinoma Cells
Bibliographic record
Abstract
Several pesticides suspected or known to have endocrine disrupting effects were screened for pro- or antiandrogenic properties by determining their effects on proliferation, prostatic-specific antigen (PSA) secretion and androgen receptor (AR) expression, and AR phosphorylation in androgen-dependent LNCaP human prostate cancer cells, as well as on the expression and catalytic activity of the enzyme CYP17 in H295R human adrenocortical carcinoma cells, an in vitro model of steroidogenesis. Effects on SRD5A gene expression were determined in both cell lines. Benomyl, vinclozolin, and prochloraz, but not atrazine, concentration dependently (1-30 μM) decreased dihydrotestosterone (DHT)-stimulated proliferation of LNCaP cells. All pesticides except atrazine decreased DHT-stimulated PSA secretion, AR nuclear accumulation, and AR phosphorylation on serines 81 and 213 in LNCaP cells. Benomyl and prochloraz, but not vinclozolin or atrazine, decreased levels of CYP17 gene and protein expression, as well as catalytic activity in H295R cells. In the case of prochloraz, some of these effects corresponded with cytotoxicity. H295R cells expressed AR protein and SRD5A1, but not SRD5A2 transcripts. SRD5A1 gene expression in H295R cells was increased by 10 nM DHT, whereas in LNCaP cells significant induction was observed by 0.1 nM DHT. AR protein expression in H295R cells was not increased by DHT. Vinclozolin decreased DHT-induced SRD5A1 gene expression in LNCaP, but not H295R cells, indicating a functional difference of AR between the cell lines. In conclusion, pesticides may exert antiandrogenic effects through several mechanisms that are cell type-specific, including AR antagonism and down-regulation or catalytic inhibition of androgen biosynthetic enzymes, such as CYP17 and SRD5A1.
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) | 0.002 | 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".