The Porcine Skin Microbiome Exhibits Broad Fungal Antagonism
Bibliographic record
Abstract
Abstract The skin and its microbiome function to protect the host from pathogen colonization and environmental stressors. In this study, using the Wisconsin Miniature Swine™ model, we characterize the porcine skin fungal and bacterial microbiomes, identify bacterial isolates displaying antifungal activity, and use whole-genome sequencing to identify biosynthetic gene clusters encoding for secondary metabolites that may be responsible for the antagonistic effects on fungi. Through this comprehensive approach of paired microbiome sequencing with culturomics, we report the discovery of novel species of Corynebacterium and Rothia . Further, this study represents the first comprehensive evaluation of the porcine skin mycobiome and the evaluation of bacterial-fungal interactions on this surface. Several diverse bacterial isolates exhibit potent antifungal properties against fungal pathogens in vitro . Genomic analysis of inhibitory species revealed a diverse repertoire of uncharacterized biosynthetic gene clusters suggesting a reservoir of novel chemical and biological diversity. Collectively, the porcine skin microbiome represents a potential unique source of novel antifungals. Highlights Porcine skin bacterial communities are consistent with previous reports on porcine and human skin. Fungal community composition resembles mycobiomes from other mammalian skin, but not human skin. Bacteria isolated from porcine skin have antimicrobial and particularly strong antifungal activity in vitro . Discovered three new Corynebacterium species and one new Rothia species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".