The role of intestinal bacteria in ammonia detoxification ability of teleost fish
Bibliographic record
Abstract
Protein catabolism during digestion generates appreciable levels of ammonia in the gastrointestinal tract (GIT) lumen. Amelioration by the enterocyte, via enzymes such as glutamine synthetase (GS), glutamate dehydrogenase (GDH), and alanine and aspartate aminotransferases (ALT; AST), is found in teleost fish. Conservation of these enzymes across bacterial phyla suggests that the GIT microbiome could also contribute to ammonia detoxification by providing supplemental activity. Hence, the GIT microbiome, enzyme activities and ammonia detoxification were investigated in two fish occupying dissimilar niches: the carnivorous rainbow darter and the algivorous central stoneroller. There was a strong effect of fish species on the activity levels of GS, GDH, AST and ALT, as well as GIT lumen ammonia concentration, and bacterial composition of the GIT microbiome. Furthermore, removal of the intestinal bacteria impacted intestinal activities of GS and ALT in the herbivorous fish but not in the carnivore. The repeatability and robustness of this relationship was tested across field locations and years. Within an individual waterbody, there was no impact of sampling location on any of these factors. However, different waterbodies affected enzyme activities and luminal ammonia concentrations in both fish, while only the central stoneroller intestinal bacteria populations varied. Overall, a relationship between GIT bacteria, enzyme activity and ammonia detoxification was observed in herbivorous fish while the carnivorous fish displayed a correlation between enzyme activity and ammonia detoxification alone that was independent of the GIT microbiome. This could suggest that carnivorous fish are less dependent on non-host mechanisms for ammonia regulation in the GIT.
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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".