Inter Individual Variations of the Fish Skin Microbiota: Host Genetics Basis of Mutualism?
Bibliographic record
Abstract
The commensal microbiota of fish skin is suspected to provide a protection against opportunist infections. The skin of fish harbors a complex and diverse microbiota that closely interacts with the surrounding water microbial communities. Up to now there is no clear evidence as to whether the host regulates the recruitment of environmental bacteria to build a specific skin microbiota. To address this question, we detected Quantitative Trait Loci (QTL) associated with the abundance of specific skin microbiota bacterial strains in brook charr (Salvelinus fontinalis), combining 16S RNA tagged-amplicon 454 pyrosequencing with genetic linkage analysis. Skin microbiota analysis revealed high inter-individual variation among 86 F2 fish progeny based upon the relative abundance of bacterial operational taxonomic units (OTUs). Out of those OTUs, the pathogenic strain Flavobacterium psychrophilum and the non-pathogenic strain Methylobacterium rhodesianum explained the majority of inter-individual distances. Furthermore, a strong negative correlation was found between Flavobacterium and Methylobacterium, suggesting a mutually competitive relationship. Finally, after considering a total of 266 markers, genetic linkage analysis highlighted three major QTL associated with the abundance of Lysobacter, Rheinheimera and Methylobacterium. All these three genera are known for their beneficial antibacterial activity. Overall, our results provide evidence that host genotype may regulate the abundance of specific genera among their surface microbiota. They also indicate that Lysobacter, Rheinheimera and Methylobacterium are potentially important genera in providing protection against pathogens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".