Genomics of Serrasalmidae teleosts through the lens of microbiome fingerprinting
Bibliographic record
Abstract
Associations between host genotype and the microbiome of holobionts have been shown in a variety of animal clades, but studies on teleosts mostly show weak associations. Our study aimed to explore these relationships in four sympatric Serrasalmidae (i.e. piranha) teleosts from an Amazonian lake, using datasets from the hosts genomes (SNPs from GBS), skin and gut microbiomes (16S rRNA metataxonomics), and diets (COI metabarcoding) from the same fish individuals. Firstly, we investigated whether there were significant covariations of microbiome and fish genotypes at the inter and intraspecific scales. We also assessed the extent of co-variation between Serrasalmidae diet and microbiome, to isolate genotypic differences from dietary effects on community structure. We observed a significant covariation of skin microbiomes and host genotypes at interspecific (R2=24.4%) and intraspecific (R2=6.2%) scales, whereas gut microbiomes correlated poorly with host genotypes. Serrasalmidae diet composition was significantly correlated to fish genotype only at the interspecific scale (R2=5.4%), but did not covary with gut microbiome composition (Mantel R=-0.04; only 6 microbiome taxa involved). Secondly, we tested whether microbial taxa represent reliable host traits to complement host genotypic variations in these species. By using an NMDS ordination-based approach, we observed that subsets of the skin and gut microbiomes selected by a machine-learning Random Forest algorithm can complement host genotypic variations by increasing significantly the average interspecific differentiation. The complementarity of genome and microbiome variations suggests that combining both markers could potentially benefit our understanding of the evolution of Serrasalmidae in future studies.
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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".