Transcriptomic, cellular and life-history responses of Daphnia magna chronically exposed to benzotriazoles: Endocrine-disrupting potential and molting effects
Bibliographic record
Abstract
Benzotriazoles (BZTs) are ubiquitous aquatic contaminants used in a wide range of industrial and domestic applications from aircraft deicers to dishwasher tablets. Acute toxicity has been reported in aquatic organisms for some of the BZTs but their mode of action remains unknown. The objectives of this study were to evaluate the transcriptomic response of D. magna exposed to sublethal doses of 1H-benzotriazole (BTR), 5-methyl-1H-benzotriazole (5MeBTR) and 5-chloro-1H-benzotriazole (5ClBTR) using RNA-sequencing and quantitative real-time PCR. Cellular and life-history endpoints (survival, number of neonates, growth) were also investigated. Significant effects on the molting frequency were observed after 21-d exposure to 5MeBTR and 5ClBTR. No effects on molting frequency were observed for BTR but RNA-seq results indicated that this BZT induced the up-regulation of genes coding for cuticular proteins, which could have compensated the molting disruption. Molting in cladocerans is actively controlled by ecdysteroid hormones. Complementary short-term temporal analysis (4- and 8-d exposure) of the transcription of genes related to molting and hormone-mediated processes indicated that the three compounds had specific modes of action. BTR induced the transcription of genes involved in 20-hydroxyecdysone synthesis, which suggests pro-ecdysteroid properties. 5ClBTR exposure induced protein activity and transcriptional levels of chitinase enzymes, associated with an impact on ecdysteroid signaling pathways, which could explain the decrease in molt frequency. Finally, 5MeBTR seemed to increase molt frequency through epigenetic processes. Overall, results suggested that molting effects observed at the physiological level could be linked to endocrine regulation impacts of BZTs at the molecular level.
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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".