Bioactivity assessment of organophosphate flame retardants via a dose-dependent yeast functional genomics approach
Bibliographic record
Abstract
Organophosphate flame retardants (OPFRs) have been widely detected in multiple environment media and have many adverse effects with complex toxicity mechanisms. However, the early molecular responses to OPFRs have not been fully elucidated, thereby making it difficult to assess their risks accurately. In this work, we systematically explored the point of departure (POD) of biological pathways at genome-wide level perturbed by 14 OPFRs with three substituents (alkyl, halogen, and aryl) using a dose-dependent functional genomics approach in Saccharomyces cerevisiae at 24 h exposure. Firstly, our results demonstrated that the overall biological potency at gene level (PODDRG20) ranged from 0.013 to 35.079 μM for 14 OPFRs, especially the tributyl phosphate (TnBP) exhibited the strongest biological potency with the least PODDRG20. Secondly, we found that structural characteristics of carbon number and logKow were significantly negatively correlated with POD, and carbon number and logKow also significantly affected lipid metabolism associated processes. Thirdly, these early biological pathways of OPFRs toxification were found to be involved in lipid metabolism, oxidative stress, DNA damage, MAPK signaling pathway, and amino acid and carbohydrate metabolism, among which the lipid metabolism was the most sensitive molecular response perturbed by most OPFRs. More importantly, we identified one resistant mutant strain with knockout of ERG2 (YMR202W) gene participated in steroid biosynthesis pathway, which can serve as a key yeast strain of OPFRs toxification. Overall, our study demonstrated an effective platform for accurately assessing OPFRs risks and provided a basis for further green OPFRs development.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".