Identification of estrogen-regulated genes by microarray analysis of the uterus of immature rats exposed to endocrine disrupting chemicals
Bibliographic record
Abstract
Environmental estrogenic compounds which bind to the estrogen receptor (ER) can block or alter endogenous functions of estrogen in reproductive and developmental stages. A microarray technology is a very valuable method for the prediction of hormone-responsive activities in various gene expressions. Thus, we investigated the altered gene expression by estrogen and endocrine disruptors (EDs) using microarray technology in the uterus of immature rats. In this study, the expression levels of only 555 genes (7.42%) among the 7636 genes spotted on microarray chips were enhanced by more than two-fold following treatment with estradiol (E2), suggesting that direct or rapid response to E2 is widespread at the mRNA levels in these genes. In addition, elevated expression levels of the genes (over 2-fold) were observed by diethylstilbestrol (DES; 9.01%), octyl-phenol (OP; 8.81%), nonyl-phenol (NP; 9.51%), bisphenol-A (BPA; 8.26%) or genistein (9.97%) in the uterus of immature rats. The expression levels of representative genes, i.e., calbindin-D9k (CaBP-9k; vitamin D-dependent calcium-binding protein), oxytocin, adipocyte complement related protein (MW 30 kDa), lactate dehydrogenase A and calcium binding protein A6 (S100a6; calcyclin), were confirmed in these tissues by real-time PCR. In addition, the mRNA levels of these genes by real-time PCR were increased at follicular phase when E2 level was elevated during estrous cycle of adult female rats. In conclusion, these results indicate distinct altered expression of responsive genes following exposure to E2 and estrogenic compounds, and implicate distinct effects of endogenous E2 and environmental endocrine disrupting chemicals in the uterus of immature rats.
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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 | 0.000 | 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".