Exhausting exercise and tissue-specific expression of monocarboxylate transporters in rainbow trout
Bibliographic record
Abstract
Transmembrane lactate movements are mediated by monocarboxylate transporters (MCTs), but these proteins have never been characterized in rainbow trout. Our goals were to clone potential trout MCTs, determine tissue distribution, and quantify the effects of exhausting exercise on MCT expression. Such information could prove important to understand the mechanisms underlying the classic "lactate retention" seen in trout white muscle after intense exercise. Four isoforms were identified and partially characterized in rainbow trout: MCT1a, MCT1b, MCT2, and MCT4. MCT1b was the most abundant in heart and red muscle but poorly expressed in the gill and brain where MCT1a and MCT2 were prevalent. MCT expression was strongly stimulated by exhausting exercise in brain (MCT2: +260%) and heart (MCT1a: +90% and MCT1b: +50%), possibly to increase capacity for lactate uptake in these highly oxidative tissues. By contrast, the MCTs of gill, liver, and muscle remained unaffected by exercise. This study provides a possible functional explanation for postexercise "lactate retention" in trout white muscle. Rainbow trout may be unable to release large lactate loads rapidly during recovery because: 1) they only poorly express MCT4, the main lactate exporter found in mammalian glycolytic muscles; 2) the combined expression of all trout MCTs is much lower in white muscle than in any other tissue; and 3) exhausting exercise fails to upregulate white muscle MCT expression. In this tissue, carbohydrates act as an "energy spring" that alternates between explosive power release during intense swimming (glycogen to lactate) and recoil during protracted recovery (slow glycogen resynthesis from local lactate).
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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".