Betaine supplementation affects carbohydrate metabolism in the skeletal muscle of finishing pigs
Bibliographic record
Abstract
This study aimed to assess the effect of betaine supplementation on the proteomic profile of the longissimus thoracis muscle in finishing pigs. Thirty-six crossbred female pigs (initial body weight = 88.96 ± 3.48 kg) were allocated in a completely randomized experimental design with two dietary treatments, with nine replications per treatment and two pigs per replication. The experimental diets consisted of a control diet (CON) and the CON diet supplemented with 2.5 g/kg of betaine (BET). At the end of the trial, one pig per unit was slaughtered, and longissimus thoracis (LT) muscle samples were analyzed via mass spectrometry to identify differentially abundant proteins (DAPs). Additionally, the intramuscular fat (IMF) content in the LT muscle was evaluated. Network analyses were performed to identify the biological processes and KEGG pathways. Results indicated that eleven DAPs were down-regulated (q-Value < 0.05) in the BET group compared to the CON group. Most DAPs were associated with carbohydrate metabolism, indicating that betaine may modulate cellular energy metabolism. These proteins were involved in glycolysis, glycogenolysis, the pentose phosphate pathway, and the glucagon signaling pathway. Moreover, pigs in the BET group had higher (P = 0.02) IMF compared to the CON group. In conclusion, betaine supplementation in finishing pigs down-regulated proteins related to carbohydrate metabolism in skeletal muscle, suppressed glucose metabolic processes and carbohydrate catabolic processes. These findings indicate alterations in glucose flow, possibly favoring other metabolic pathways, such as the pentose phosphate pathway. Furthermore, betaine supplementation increased IMF deposition and improved meat tenderness.
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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".