Evolutionary investigations of the biosynthetic diversity in the skin microbiome using lsaBGC
Bibliographic record
Abstract
Abstract We developed lsa BGC, a bioinformatics suite that introduces several new methods to expand on the available infrastructure for genomic and metagenomic-based comparative and evolutionary investigation of biosynthetic gene clusters (BGCs). Through application of the suite to four genera commonly found in skin microbiomes, we uncover multiple novel findings on the evolution and diversity of their BGCs. We show that the virulence associated carotenoid staphyloxanthin in Staphylococcus aureus is ubiquitous across the Staphylococcus genus but has largely been lost in the skin-commensal species Staphylococcus epidermidis . We further identify thousands of novel single nucleotide variants (SNVs) within BGCs from the Corynebacterium tuberculostearicum sp. complex, which we describe here to be a narrow, multi-species clade that features the most prevalent Corynebacterium in healthy skin microbiomes. Although novel SNVs were approximately ten times as likely to correspond to synonymous changes when located in the top five percentile of conserved sites, lsaBGC identified SNVs which defied this trend and are predicted to underlie amino acid changes within functionally key enzymatic domains. Ultimately, beyond supporting evolutionary investigations, lsa BGC provides important functionalities to aid efforts for the discovery or synthesis of natural products.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".