Gene Expression Profiles for Detecting and Distinguishing Potential Endocrine-Disrupting Compounds in Environmental Samples
Bibliographic record
Abstract
Industrial and municipal processes may produce and release endocrine-disrupting compounds (EDCs) into the environment, but the exact nature of their effects is difficult to investigate. EDCs typically exert their effect by affecting gene expression aberrantly. To determine if gene expression profiles could be used to detect and distinguish estrogenic EDCs, an estrogen receptor positive human breast cancer cell line (MCF-7) was exposed to known estrogenic compounds, suspected EDCs, and extracts from three effluent samples. A set of specifically estrogen-regulated genes was identified by microarray analysis. Nine estrogen up-regulated genes (IGFBP4, HSPA8, B4GALT1, XBP1, KRT8, GTPBP4, HNRPAB, SLC2A1, and CALM1) and two estrogen down-regulated genes (ID2 and ZNF217) were consistently detectable in response to estrogen and other estrogenic compounds. Gene expression patterns in cells that were exposed to effluent sample extracts were compared to gene expression patterns in cells that were exposed to known endocrines. Using this technique, two of the effluent samples were shown to have estrogenic activity. This approach could easily be extended to screen for other types of receptor-mediated endocrine disruption. For example, cells expressing androgen or aryl hydrocarbon receptors could be used in gene expression profiling assays to detect androgenic effects or for the presence of bioactive aromatic hydrocarbons. Gene expression profiling is emerging as a sensitive and specific method to screen complex samples for endocrine disrupting activity.
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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 | 0.002 | 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) | 0.001 | 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".