Effects of toxic Microcystis aeruginosa on the silver carp Hypophthalmichtys molitrix revealed by hepatic RNA-seq and miRNA-seq
Bibliographic record
Abstract
High-throughput sequencing was applied to analyze the effects of toxic Microcystis aeruginosa on the silver carp Hypophthalmichthys molitrix. Silver carps were exposed to two cyanobacteria species (toxic and non-toxic) for RNA-seq and miRNA-seq analysis. RNA-seq revealed that the liver tissue contained 105,379 unigenes. Of these genes, 143 were significantly differentiated, 82 were markedly up-regulated, and 61 were remarkably down-regulated. GO term enrichment analysis indicated that 35 of the 154 enriched GO terms were significantly enriched. KEGG pathway enrichment analysis demonstrated that 17 of the 118 enriched KEGG pathways were significantly enriched. A considerable number of disease/immune-associated GO terms and significantly enriched KEGG pathways were also observed. The sequence length determined by miRNA-seq was mainly distributed in 20-23 bp and composed of 882,620 unique small RNAs, and 53% of these RNAs were annotated to miRNAs. As confirmed, 272 known miRNAs were differentially expressed, 453 novel miRNAs were predicted, 112 miRNAs were well matched with 7,623 target genes, and 203 novel miRNAs were matched with 15,453 target genes. qPCR also indicated that Steap4, Cyp7a1, CABZ01088134.1, and PPP1R3G were significantly differentially expressed and might play major roles in the toxic, detoxifying, and antitoxic mechanisms of microcystin in fish.
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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".