Gut microbiota analysis of Blenniidae fishes including an algae-eating fish and clear boundary formation among isolated Vibrio strains
Bibliographic record
Abstract
Some marine fishes are algae-feeding, and the microorganisms in their digestive tracts produce carbohydrate hydrolyzing enzymes such as agarose and fucosidase, which are potentially interesting resource for new functional enzymes. The purpose of this study was to establish a method for identifying and utilizing characteristic bacteria from the intestines of two algae-eating fish species: Andamia tetradactylus, which exclusively eats algae on the rock surface, and stellar rockskipper Entomacrodus stellifer, which feeds on both algae and invertebrates. We tested the species composition of the intestinal bacterial flora and found that Proteobacteria were commonly found both in species as in the common gut communities of marine fish, whereas Spirochaetes and Tenericutes occupied the flora of A. tetradactylus. We then performed anaerobic and aerobic cultures and isolated 34 and 44 strains including 48 strains belonged to Vibrio species from A. tetradactylus and E. stellifer. We observed that some Vibrio strains formed a clear boundary to avoid contacting other strains of bacteria. Whole-genome sequencing of such two Vibrio alginolyticus strains revealed two cyclic chromosomes commonly found in the genome of Vibrio species, and some unique genes encoding alginate lyase, chitinases, and type I-F CRISPR-associated endoribonuclease for the first time in Vibrio alginolyticus.
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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".