PGC-1α increases skeletal muscle lactate uptake by increasing the expression of MCT1 but not MCT2 or MCT4
Bibliographic record
Abstract
We examined the relationship between PGC-1alpha protein; the monocarboxylate transporters MCT1, 2, and 4; and CD147 1) among six metabolically heterogeneous rat muscles, 2) in chronically stimulated red (RTA) and white tibialis (WTA) muscles (7 days), and 3) in RTA and WTA muscles transfected with PGC-1alpha-pcDNA plasmid in vivo. Among rat hindlimb muscles, there was a strong positive association between PGC-1alpha and MCT1 and CD147, and between MCT1 and CD147. A negative association was found between PGC-1alpha and MCT4, and CD147 and MCT4, while there was no relationship between PGC-1alpha or CD147 and MCT2. Transfecting PGC-1alpha-pcDNA plasmid into muscle increased PGC-1alpha protein (RTA +23%; WTA +25%) and induced the expression of MCT1 (RTA +16%; WTA +28%), but not MCT2 and MCT4. As a result of the PGC-1alpha-induced upregulation of MCT1 and its chaperone CD147 (+29%), there was a concomitant increase in the rate of lactate uptake (+20%). In chronically stimulated muscles, the following proteins were upregulated, PGC-1alpha in RTA (+26%) and WTA (+86%), MCT1 in RTA (+61%) and WTA (+180%), and CD147 in WTA (+106%). In contrast, MCT4 protein expression was not altered in either RTA or WTA muscles, while MCT2 protein expression was reduced in both RTA (-14%) and WTA (-10%). In these studies, whether comparing oxidative capacities among muscles or increasing their oxidative capacities by PGC-1alpha transfection and chronic muscle stimulation, there was a strong relationship between the expression of PGC-1alpha and MCT1, and PGC-1alpha and CD147 proteins. Thus, MCT1 and CD147 belong to the family of metabolic genes whose expression is regulated by PGC-1alpha in skeletal muscle.
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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".