Profiling Hepatic microRNAs in Zebrafish: Fluoxetine Exposure Mimics a Fasting Response That Targets AMP-Activated Protein Kinase (AMPK)
Bibliographic record
Abstract
This study examined the similarities in microRNA profiles between fasted and fluoxetine (FLX) exposed zebrafish and downstream target transcripts and biological pathways. Using a custom designed microarray targeting 270 zebrafish miRNAs, we identified 9 differentially expressed miRNAs targeting transcripts in biological pathways associated with anabolic metabolism, such as adipogenesis, cholesterol biosynthesis, triacylglycerol synthesis, and insulin signaling. Exposure of female zebrafish to 540 ng/L FLX, an environmentally relevant concentration and a known metabolic repressor, increased specific miRNAs indicating greater inhibition of these pathways in spite of continued feeding. Further examination revealed two specific miRNAs, dre-let-7d and dre-miR-140-5p, were predicted in silico to bind to a primary regulator of metabolism, adenosine monophosphate-activated protein kinase (AMPK), and more specifically the two isoforms of the catalytic subunit, AMPKα1 and α2, respectively. Real-time analysis of the relative transcript abundance of the α1 and α2 mRNAs indicated a significant inverse relationship between specific miRNA and target transcript. This suggests that AMPK-related pathways may be compromised during FLX exposure as a result of increased miRNA abundance. The mechanism by which FLX regulates miRNA abundance is unknown but may be direct at the liver. The serotonin transporter, slc6a4, is the target of FLX and other selective serotonin reuptake inhibitors (SSRI) and it was found to be expressed in the liver, although treatment did not alter expression of this transporter. Exposure to FLX disrupts key hepatic metabolic pathways, which may be indicative of reduced overall fitness and these effects may be linked to specific miRNA abundance. This has important implications for the heath of fish because concentrations of SSRIs in aquatic ecosystems are continually increasing.
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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".