Succession and Fermentation Products of Grass Carp (Ctenopharyngodon idellus) Hindgut Microbiota in Response to an Extreme Dietary Shift
Bibliographic record
Abstract
Dietary intake affects the structure and function of microbes in host intestine. However, the succession of gut microbiota in response to changes in macronutrient levels during a long period of time remains insufficiently studied. Here, we determined the succession and metabolic products of intestinal microbiota in grass carp (Ctenopharyngodon idellus) undergoing an abrupt and extreme diet change, from fish meal to Sudan grass (Sorghum sudanense). Grass carp hindgut microbiota responded rapidly to the diet shift, reaching a new equilibrium approximately within 11 days. In comparison to animal-diet samples, Bacteroides, Lachnospiraceae and Erysipelotrichaceae increased significantly while Cetobacterium decreased significantly in plant-diet samples. Cetobacterium was negatively correlated with Bacteroides, Lachnospiraceae and Erysipelotrichaceae, while Bacteroides was positively correlated with Lachnospiraceae. Predicted glycoside hydrolase and polysaccharide lyase genes in Bacteroides and Lachnospiraceae from the Carbohydrate-Active enZymes (CAZy) database might be involved in degradation of the plant cell wall polysaccharides. However, none of these enzymes was detected in the grass carp genome searched against dbCAN database. Additionally, a significant decrease of short chain fatty acids (SCFA) levels in plant-based samples was observed. Generally, our results suggest a rapid adaption of grass carp intestinal microbiota to dietary shift, and that microbiota are likely to play an indispensable role in nutrient turnover and fermentation.
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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 | 0.001 | 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".