Association between the skin microbiome and <scp>MHC</scp> class <scp>II</scp> diversity in an amphibian
Bibliographic record
Abstract
Microbiomes play an important role in determining the ecology and behaviour of their hosts. However, questions remain pertaining to how host genetics shape microbiomes, and how microbiome composition influences host fitness. We explored the effects of geography, evolutionary history and host genetics on the skin microbiome diversity and structure in a widespread amphibian. More specifically, we examined the association between bacterial diversity and composition and the major histocompatibility complex class II exon 2 diversity in 12 moor frog (Rana arvalis) populations belonging to two geographical clusters that show signatures of past and ongoing differential selection. We found that while bacterial alpha diversity did not differ between the two clusters, MHC alleles/supertypes and genetic diversity varied considerably depending on geography and evolutionary history. Bacterial alpha diversity was positively correlated with expected MHC heterozygosity and negatively with MHC nucleotide diversity. Furthermore, bacterial community composition showed significant variation between the two geographical clusters and between specific MHC alleles/supertypes. Our findings emphasize the importance of historical demographic events on hologenomic variation and provide new insights into how immunogenetic host variability and microbial diversity may jointly influence host fitness with consequences for disease susceptibility and population persistence.
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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".