Getting over it? A proteomic analysis of mechanisms driving multigenerational acclimation to organic ultraviolet filters in Daphnia magna
Bibliographic record
Abstract
Organic ultraviolet filters (UVFs) such as avobenzone, octocrylene, and oxybenzone are contaminants of concern due to their widespread occurrence in aquatic environments. Previous research has demonstrated that these UVFs are toxic to aquatic invertebrates over single generation exposures; however, data regarding long-term outcomes across generations of exposure are lacking. This study sought to identify the mechanisms of toxicity to novel UVF exposure in D. magna and subsequent responses across 5 generations of continuous exposure by quantifying proteomic changes at the end of the F0, F1 and F3 generations. A parallel study observed toxicity to novel UVF exposure (>40 % mortality, 46 % decreased reproduction); however, toxic effects were absent after 3 generations of continuous exposure. Impaired metabolism and immune response processes were observed in the F0 generation, with decreased abundance in >80 % of altered proteins in octocrylene and oxybenzone exposures. Impairment of these processes were gradually reversed over subsequent generations, with >60 % of altered proteins demonstrating increased abundance by the F3 generation. An increase in chitin production that could reduce membrane permeability to xenobiotics and pathogens, along with subtle changes in metabolic processes may allow exposed populations to negate many of the negative effects associated with UVF exposure. These results offer mechanistic insights into the gradual acclimation of continuously exposed D. magna populations that have been observed in response to a variety of contaminants and further serve to highlight the importance of utilizing a long-term approach for studies seeking to model contamination risks in wild populations.
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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) | 0.000 | 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".