Effect of muscle fibre types and carnosine levels on the expression of carnosine-related genes in pig skeletal muscle
Bibliographic record
Abstract
It is generally accepted that carnosine (β-alanyl-L-histidine) content is higher in glycolytic than in oxidative muscle fibres, but the underlying mechanisms responsible for this difference remain to be elucidated. A first study to better understand potential mechanisms involved was undertaken (1) to determine whether differences in the expression of carnosine-related enzymes (CARNS1, CNDP2) and transporters (SLC6A6, SLC15A3, SLC15A4, SLC36A1) exist between oxidative and glycolytic myofibres and (2) to study the effect of carnosine on myoblast proliferative growth and on carnosine-related gene expression in cultured myoblasts isolated from glycolytic and oxidative muscles. Immunohistochemistry analyses were conducted to determine the cellular localization of carnosine-related proteins. Laser-capture microdissection and qPCR analyses were performed to measure the expression of carnosine-related genes in different myofibres isolated from the longissimus dorsi muscle of ten crossbred pigs. Myogenic cells originating from glycolytic and oxidative muscles were cultured to assess the effect of carnosine (0, 10, 25 and 50 mM) on their proliferative growth and on carnosine-related gene expression. The mRNA abundance of CNDP2 and of the studied carnosine transporters was higher in oxidative than in glycolytic myofibres. Since carnosine synthase (CARNS1) mRNA abundance was not affected by either the fibre type or the addition of carnosine to myoblasts, its transcriptional regulation would not be the main process by which carnosine content differences are determined in oxidative and glycolytic muscles. The addition of carnosine to myoblasts leading to a dose-dependent increase in SLC15A3 transcripts, however, suggests a role for this transporter in carnosine uptake and/or efflux to maintain cellular homeostasis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".