Deciphering taxonomic and functional patterns of microbial communities associated with the tiger tail seahorse (Hippocampus comes)
Bibliographic record
Abstract
Abstract This study aimed to explore the microbial diversity and metabolic functions of the skin and gut of the tiger tail seahorse (Hippocampus comes) and their surrounding environment using shotgun metagenomics and bioinformatics. Members belonging to the Pseudomonadota phylum were dominant on the skin, whereas Bacteroidota was dominant in the gut. Bacillota, Actinomycetota, and Planctomycetota were also detected in the seahorse-associated microbiome. Statistical analysis revealed significant differences (p < 0.01) in species diversity between skin and gut microbiomes, with members belonging to the Moraxellaceae family being dominant on the skin and the Bacteroidaceae family in the gut. Moreover, the surrounding environment (water or sediment) did not have a direct effect on the seahorse microbiome composition. Functional annotations highlighted the involvement of the skin microbiome in energy, lipid, and amino acid metabolism, as well as terpenoids and polyketides metabolism, xenobiotics biodegradation and metabolism, and cellular processes. Additionally, annotations indicated the presence of quorum sensing and intercellular communication. The relative abundance of bacteriocins was similar in both gut and skin, which is significant in shaping microbial communities due to their antimicrobial properties. Overall, the study highlights the importance of seahorse-microbe relationship for their well-being and holds implications for conservation and sustainable aquaculture.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".