A comparative study of 3 alternative avian toxicity testing methods: Effects on hepatic gene expression in the chicken embryo
Bibliographic record
Abstract
There is growing interest in developing alternative methods to screen and prioritize chemical hazards, although few studies have compared responses across different methods. The objective of the present study was to compare 3 alternative liver methods derived from white Leghorn chicken (Gallus gallus domesticus): primary hepatocyte culture, liver slices, and liver from in ovo injected embryos. We examined hepatic gene expression changes after exposure to 3 chemicals (17β-trenbolone [17βT], 17β-estradiol [E2], and 2,3,7,8-tetrachlorodibenzo-p-dioxin [TCDD]) using a custom quantitative polymerase chain reaction (qPCR) array with 7 genes (vitellogenin [VTG], apolipoprotein [Apo], cytochrome P450 1A4 [CYP1A4], liver basic fatty acid binding protein [LBFABP], 3β hydroxysteroid dehydrogenase [HSD3β1], stearoyl coenzyme A desaturase [SCD], and estrogen sulfotransferase [SULT1E1]). Gene expression across the 3 methods was examined using hierarchical clustering. Up-regulation of CYP1A4 in response to TCDD was consistent across all methods, and the magnitude was higher in hepatocytes (>150-fold) compared with slices (>31-fold) and in ovo liver (>27-fold). In hepatocytes, SCD and VTG up-regulation in response to 17βT and E2 was >4-fold and 16-fold, respectively. The rank order of cases with significant changes in gene expression among the 3 methods was: hepatocytes (22) > in ovo liver (11) > liver slices (6). Hierarchical clustering grouped liver slices and in ovo liver as more similar, whereas hepatocytes were grouped separately from in ovo liver. More introspective comparisons are needed to understand how and why alternative methods differ and to aid in their integration into toxicity testing. Environ Toxicol Chem 2019;38:2546-2555. © 2019 SETAC.
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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".