Deciphering mechanisms of UV filter- and temperature-induced bleaching in the coral <i>Acropora tenuis</i> , using ecotoxicogenomics
Bibliographic record
Abstract
Abstract Coral reefs are at risk of bleaching due to various environmental and anthropogenic stressors such as global warming and chemical pollutants. However, there is little understanding of stressor-specific mechanisms that cause coral bleaching. Therefore, conducting accurate ecotoxicological risk assessments and deciphering modes of action of potentially deleterious ultraviolet (UV) filters (sunscreen compounds) are crucial issues. In this study, we evaluated the toxicity and bleaching effect of benzophenone-3 (BP-3), which is widely used in sunscreen products, on the reef-building coral Acropora tenuis . Furthermore, to understand differences in UV filter- and temperature-induced bleaching, a comparative ecotoxicogenomic approach using RNA-seq was integrated into a toxicity test to clarify differences in gene expression changes induced by BP-3 and heat stress (31°C). The lethal concentration 50% (LC50) was calculated as 3.9 mg/L, indicating that emission level of BP-3 was properly controlled based on the risk assessment. Differentially expressed genes related to oxidative stress and extracellular matrix organization were involved in coral responses to both BP-3 and heat stress, but their patterns differed. Whereas immune and heat-shock responses were activated in response to heat stress, activation of a drug metabolism pathway and several signal transduction pathways were identified in BP-3 treatment groups. Our study enhances understanding of stress responses in corals induced by UV filters and thermal stress. Using potential gene markers identified in this study for eco-epidemiological surveys of stressed corals, we urgently need to develop effective countermeasures.
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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".