Reference gene considerations for toxicological assessment of the flame retardant triphenyl phosphate in an in vitro fish embryonic model
Bibliographic record
Abstract
The reliability of relative quantification RT-qPCR depends upon the gene of interest being normalized to one or more reference genes, with the assumption that the chosen reference genes do not experience altered expression with experimental conditions. The correct choice of stable reference genes is critical when investigating alterations to gene transcript levels following exposure to endocrine and metabolic disrupting chemicals, such as the flame retardant triphenyl phosphate (TPhP). This study assessed the stability of eight reference genes following TPhP exposure in embryonic cells derived from rainbow trout (Oncorhynchus mykiss). The genes β-actin (actb) and 18s rRNA (18s) were stable, while glyceraldehyde-3-phosphate dehydrogenase (gapdh) relative expression was found to be increased. gapdh is a popular reference gene and has been previously used in the literature for investigating TPhP exposure in teleost fish models. We discuss the implications of gapdh upregulation in the context of TPhP as a metabolic disrupting chemical. Furthermore, we quantified the expression of the tumor suppressor gene p53 following TPhP exposure in relation to different reference genes to use as an example to report on how discrepancies in findings might arise depending on the stability of the chosen reference gene.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".