Comparative toxicological assessment of 2 bisphenols using a systems approach: evaluation of the behavioral and transcriptomic responses of <i>Danio rerio</i> to bisphenol A and tetrabromobisphenol A
Bibliographic record
Abstract
The zebrafish (Danio rerio) is becoming a critical component of new approach methods (NAMs) in chemical risk assessment. As a whole organism in vitro NAM, the zebrafish model offers significant advantages over individual cell-line testing, including toxicokinetic and toxicodynamic competencies. A transcriptomic approach not only allows for insight into mechanism of action for both apical endpoints and unobservable adverse outcomes, but also changes in gene expression induced by lower, environmentally relevant concentrations. In this study, we used a larval zebrafish model to assess the behavioral and transcriptomic alterations caused by subphenotypic concentrations of 2 chemicals with the same structural backbone, the endocrine-disrupting chemicals bisphenol A and tetrabromobisphenol A. Following assessment of behavioral toxicity, we used a transcriptomic approach to identify molecular pathways associated with previously described phenotypes. We also determined the transcriptomic point of departure for each chemical by modeling gene expression changes as continuous systems which allows for the identification of a single concentration at which toxic effects can be predicted. This can then be investigated with confirmatory cell-based testing in an integrated approach to testing and assessment to determine risk to human health and the environment with greater confidence. This paper demonstrates the impact of using a multi-faceted approach for evaluating the physiological and neurotoxic effects of exposure to structurally related chemicals. By comparing phenotypic effects with transcriptomic outcomes, we were able to differentiate, characterize, and rank the toxicities of related bisphenols, which demonstrates methodological advantages unique to the larval zebrafish NAM.
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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) | 0.001 | 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 | 0.001 | 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".