Social status regulates the hepatic miRNAome in rainbow trout: Implications for posttranscriptional regulation of metabolic pathways
Bibliographic record
Abstract
Juvenile rainbow trout develop social hierarchies when held in dyads, and the development of socially subordinate (SS) and social dominance (SD) phenotypes in this context has been linked to specific changes in the hepatic energy metabolism of all major macronutrients. Following our recently reported finding that transcript abundance of drosha, a key component of the microRNA (miRNA) biogenesis pathway, is increased in paired juvenile rainbow trout irrespective of social status compared to socially isolated (SI) controls, we here determined global changes of the hepatic miRNA pathway genes in detail at the transcript and protein level. Both SD and SS rainbow trout exhibited increased Ago2 protein abundance compared to SI rainbow trout, suggesting that hepatic miRNA function is increased in rainbow trout maintained in dyads. Given the well-described differences in hepatic intermediary metabolism between SD and SS rainbow trout, and the important role of miRNAs in the posttranscriptional regulation of metabolic pathways, we also identified changes in hepatic miRNA abundance between SS and SD rainbow trout using small RNA next generation sequencing. We identified a total of 24 differentially regulated miRNAs, with 15 miRNAs that exhibited increased expression, and 9 miRNAs that exhibited decreased expression in the liver of SS trout compared to SD trout. To identify potential miRNA-dependent posttranscriptional regulatory pathways important for social status-dependent regulation of hepatic metabolism in rainbow trout, we used an in silico miRNA target prediction and pathway enrichment approach. We identified enrichment for pathways related to metabolism of carbohydrates, lipids and proteins in addition to organelle-specific processes involved in energy metabolism, especially mitochondrial fusion and fission. Select predicted miRNA-mRNA target pairs within these categories were quantitatively analyzed by real-time RT-PCR to validate candidates for future studies that will probe the functional metabolic roles of specific hepatic miRNAs in the development of SD and SS metabolic phenotypes.
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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".