Effect of copper and nickel exposure on ribosomal DNA variation in <i>Daphnia pulex</i> mutation accumulation lines
Bibliographic record
Abstract
The release of heavy metals from industrial, agricultural, and mining activities poses significant risks to aquatic ecosystems by degrading water quality and generating reactive oxygen species that can damage DNA in aquatic organisms. Daphnia is a widespread keystone species in freshwater ecosystems that is routinely exposed to a range of anthropogenic and natural stressors. With a fully sequenced genome, a well-understood life history and ecology, and an extensive library of responses to toxicity, Daphnia serves as an ideal model organism for studying the impact of environmental stressors on genomic stability. Ribosomal DNA (rDNA) encodes ribosomal RNA (rRNA), which is essential for protein synthesis, and the spacers that separate the rRNA genes contain regulatory elements. However, the effects of heavy metals on this region of the genome are not well documented. We used short-read whole-genome sequences to analyze copy number and sequence variation in Daphnia pulex mutation accumulation lines exposed to nickel and copper, both individually and in combination, at concentrations that mimic levels often found at contaminated sites. We found no significant direct effect of chronic exposure to either metal on rDNA copy number or sequence variation. However, the results suggest that nickel and copper exposure may indirectly influence rDNA by altering recombination rates. We also emphasize the importance of interval length between generational samples for accurately assessing the frequency and magnitude of rDNA copy number changes. In addition, we observed differential expansion of rDNA haplotypes, suggesting that they may be clustered within the rDNA array.
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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".