Lactate signaling and fuel selection in rainbow trout: mobilization of energy reserves
Bibliographic record
Abstract
Lactate is now recognized as a regulator of fuel selection in mammals because it inhibits lipolysis by binding to the hydroxycarboxylic acid receptor 1 (HCAR1). The goals of this study were to quantify the effects of exogenous lactate on: 1) lipolytic rate or rate of appearance of glycerol in the circulation ( Ra glycerol) and hepatic glucose production ( Ra glucose), and 2) key tissue proteins involved in lactate signaling, glucose transport, glycolysis, gluconeogenesis, lipolysis, and β-oxidation in rainbow trout. Measurements of fuel mobilization kinetics show that lactate does not affect lipolysis as it does in mammals ( Ra glycerol remains at 7.3 ± 0.5 µmol·kg−1·min−1), but strongly reduces hepatic glucose production (16.4 ± 2.0 to 8.9 ± 1.2 µmol·kg−1·min−1). This reduction is likely induced by decreasing gluconeogenic flux through the inhibition of cytosolic phosphoenolpyruvate carboxykinase (Pck1, alternatively called Pepck1; 60% and 24% declines in gene expression and protein level, respectively). It is also caused by lactate substituting for glucose as a fuel in all tissues except white muscle that increases glut4a expression and has limited capacity for monocarboxylate transporter (Mct)-mediated lactate import. We conclude that lipolysis is not affected by hyperlactatemia because trout show no activation of autocrine Hcar1 signaling (gene expression of the receptor is unchanged or even repressed in red muscle). Lactate regulates fuel mobilization via Pck1-mediated suppression of gluconeogenesis and by replacing glucose as a fuel. This study highlights important functional differences in the Hcar1 signaling system between fish and mammals for the regulation of fuel selection.
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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 | 0.001 |
| 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".