Large-scale investigation for antimicrobial activity reveals novel defensive species across the healthy skin microbiome
Bibliographic record
Abstract
The human skin microbiome constitutes a dynamic barrier that can impede pathogen invasion by producing antimicrobial natural products. Gene clusters encoding for production of secondary metabolites, biosynthetic gene clusters (BGCs), that are enriched in the human skin microbiome relative to other ecological settings, position this niche as a promising source for new natural product mining. Here, we introduce a new human microbiome isolate collection, the EPithelial Isolate Collection (EPIC). It includes a large phylogenetically diverse set of human skin-derived bacterial strains from eight body sites. This skin collection, consisting of 980 strains is larger and more diverse than existing resources, includes hundreds of rare and low-abundance strains, and hundreds of unique BGCs. Using a large-scale co-culture screen to assess 8,756 pairwise interactions between skin-associated bacteria and potential pathogens, we reveal broad antifungal activity by skin microbiome members. Integrating 287 whole isolate genomes and 268 metagenomes from sampling sites demonstrates that while the distribution of BGC types is stable across body sites, specific gene cluster families (GCFs), each predicted to encode for a distinct secondary metabolite, can substantially vary. Sites that are dry or rarely moist harbor the greatest potential for discovery of novel bioactive metabolites. Among our discoveries are four novel bacterial species, three of which exert significant and broad-spectrum antifungal activity. This comprehensive isolate collection advances our understanding of the skin microbiomes biosynthetic capabilities and pathogen-fighting mechanisms, opening new avenues towards antimicrobial drug discovery and microbiome engineering.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".