Metabolites, gene expression, and gut microbiota profiles suggest the putative mechanisms via which dietary creatine increases the serum taurine and g-ABA contents in Megalobrama amblycephala
Bibliographic record
Abstract
Abstract High carbohydrate diets can affect the growth and metabolism of fish; e.g. decrease the concentration of liver betaine and cause disturbances in the creatine pathway, and damage the liver. Previous studies have shown that dietary betaine can effectively alleviate these negative effects. The aim of this study was to explore the effects of creatine on growth performance, liver health status, metabolites and gut microbiota in M. amblycephala. The results showed that supplementing creatine and betaine together reduced the feed conversion ratio significantly (P < 0.05, compared to CD and HCD) and improved liver health (compared to HCD). Compared with the BET group, dietary creatine significantly increased the abundances of Firmicutes, Bacteroidota, ZOR0006 and Bacteroides, and decreased the abundances of Proteobacteria, Fusobacteriota, Vibrio, Crenobacter, and Shewanella in the CRE1 group. Dietary creatine increased the content of taurine, arginine, ornithine, γ-aminobutyric acid (g-ABA) and creatine (CRE1 vs. BET group), and the expression of creatine kinase (ck), sulfinoalanine decarboxylase (csad), guanidinoacetate N-methyltransferase (gamt), glycine amidinotransferas (gatm), agmatinase (agmat), diamine oxidase1 (aoc1), and glutamate decarboxylase (gad) in the CRE1 group. Overall, these results suggested that dietary supplementation of creatine (0.5% − 2%) did not affect the growth performance, but it altered the gut microbial composition at the phylum and genus levels; it also increased the serum content of taurine by enhancing the activities of creatine metabolism and the CSA pathway, and increased the serum content of g-ABA by enhancing the activities of arginine metabolism, putrescine synthesis, and synthesis of g-ABA.
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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".