Transcriptomic analysis to elucidate the effects of high stocking density on grass carp (Ctenopharyngodon idella)
Bibliographic record
Abstract
BACKGROUND: Grass carp (Ctenopharyngodon idella) is one of the most widely cultivated fishes in China. High stocking density can reportedly affect fish growth and immunity. Herein we performed PacBio long-read single-molecule real-time (SMRT) sequencing and Illumina RNA sequencing to evaluate the effects of high stocking density on grass carp transcriptome. RESULTS: SMRT sequencing led to the identification of 33,773 genes (14,946 known and 18,827 new genes). From the structure analysis, 8,009 genes were detected with alternative splicing events, 10,219 genes showed alternative polyadenylation sites and 15,521 long noncoding RNAs. Further, 1,235, 962, and 213 differentially expressed genes (DEGs) were identified in the intestine, muscle, and brain tissues, respectively. We performed functional enrichment analyses of DEGs, and they were identified to be significantly enriched in nutrient metabolism and immune function. The expression levels of several genes encoding apolipoproteins and activities of enzymes involved in carbohydrate enzymolysis were found to be upregulated in the high stocking density group, indicating that lipid metabolism and carbohydrate decomposition were accelerated. Besides, four isoforms of grass carp major histocompatibility complex class II antigen alpha and beta chains in the aforementioned three tissue was showed at least a 4-fold decrease. CONCLUSIONS: The results suggesting that fish farmed at high stocking densities face issues associated with the metabolism and immune system. To conclude, our results emphasize the importance of maintaining reasonable density in grass carp aquaculture.
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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 | 0.001 |
| 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".