Genomics of Serrasalmidae teleosts through the lens of microbiome fingerprinting
Bibliographic record
Abstract
Abstract Associations between host genotype and host‐associated microbiomes 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 data sets from the hosts genomes (single nucleotide polymorphisms from genotyping by sequencing), skin and gut microbiomes (16S rRNA gene metataxonomics) and diets (COI metabarcoding) from the same fish individuals. First, we investigated whether there were significant covariations of microbiome and fish genotypes at the inter‐ and intraspecific levels. We also assessed the extent of covariation between Serrasalmidae diet and microbiome, to isolate genotypic 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%) levels, whereas gut microbiomes correlated poorly with host genotypes. Serrasalmidae diet composition was significantly correlated to fish genotype only at the interspecific level (R2 = 5.4%), but did not covary with gut microbiome composition (Mantel R = −.04). Second, we investigated whether the study of interspecific differentiation could benefit from considering host‐associated microbial communities in addition to host genotypes. By using a nonmetric multidimensional scaling (NMDS) ordination‐based approach, we observed that ordinations from skin‐ and gut species‐specific bacterial biomarkers identified through a random forest algorithm could significantly increase the average interspecific differentiation detected through host genotype data alone. Although future studies encompassing additional species and environments are needed, our results suggest Serrasalmidae microbiomes could constitute an insightful trait to be considered when studying the interspecific differences between members of this clade.
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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 | 0.001 |
| 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".