Identification and expression of microRNAs in european eels Anguilla anguilla from two natural sites with different pollution levels
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of small non-coding RNA that control multiple biological processes through negative post-transcriptional regulation of gene expression. Recently a role of miRNAs in the response of aquatic organisms to environmental toxicants emerged. Toxicant-induced changes in miRNA expression might then represent novel biomarkers to evaluate the health status of these organisms. In this study, we aimed to identify the miRNA repertoire in the liver of the European eel Anguilla anguilla and to compare their differential expression between a polluted site located in the Gironde Estuary and a pristine site in Arcachon Bay (France). A total of 299 mature miRNAs were identified. In polluted water, 19 miRNAs were up-regulated and 22 were down-regulated. We predicted that these differentially expressed miRNAs could target 490 genes that were involved in ribosome biogenesis, response to hormones, response to chemical and chromatin modification. Moreover, we observed only few examples (29) of negative correlation between the expression levels of miRNAs and their targets suggesting that, in the system studied, miRNAs might not only regulate gene expression directly by degrading mRNA but also by inhibiting protein translation or by regulating other epigenetic processes. This study is the first example of in situ investigation of the role of miRNAs in the response of a fish species to water quality. Our findings provide new insights into the involvement of epigenetic mechanisms in the response of animals chronically exposed to pollution and pave the way for the utilization of miRNAs in aquatic ecotoxicology.
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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 | 0.001 | 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".