Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia
Bibliographic record
Abstract
Skin is the largest organ of the body and represents the primary physical barrier between mammals and their external environment, yet the factors that govern skin microbial community composition among mammals are poorly understood. The objective of this research was to generate a skin microbiota baseline for members of the class Mammalia, testing the effects of host species, geographic location, body region, and biological sex. Skin from the back, torso, and inner thighs of 177 nonhuman mammals was sampled, representing individuals from 38 species and 10 mammalian orders. Animals were sampled from farms, zoos, households, and the wild. The DNA extracts from all skin swabs were amplified by PCR and sequenced, targeting the V3-V4 regions of bacterial and archaeal 16S rRNA genes. Previously published skin microbiome data from 20 human participants, sampled and sequenced using an identical protocol to the nonhuman mammals, were included to make this a comprehensive analysis. Human skin microbial communities were distinct and significantly less diverse than all other sampled mammalian orders. The factor most strongly associated with microbial community data for all samples was whether the host was a human. Within nonhuman samples, host taxonomic order was the most significant factor influencing skin microbiota, followed by the geographic location of the habitat. By comparing the congruence between host phylogeny and microbial community dendrograms, we observed that Artiodactyla (even-toed ungulates) and Perissodactyla (odd-toed ungulates) had significant congruence, providing evidence of phylosymbiosis between skin microbial communities and their hosts.
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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".