Identification of the lipodepsipeptide selethramide encoded in a giant nonribosomal peptide synthetase from a <i>Burkholderia</i> bacterium
Bibliographic record
Abstract
Bacterial natural products have found many important industrial applications. Yet traditional discovery pipelines often prioritize individual natural product families despite the presence of multiple natural product biosynthetic gene clusters in each bacterial genome. Systematic characterization of talented strains is a means to expand the known natural product space. Here, we report genomics, epigenomics, and metabolomics studies of Burkholderia sp. FERM BP-3421, a soil isolate and known producer of antitumor spliceostatins. Its genome is composed of two chromosomes and two plasmids encoding at least 29 natural product families. Metabolomics studies showed that FERM BP-3421 also produces antifungal aminopyrrolnitrin and approved anticancer romidepsin. From the orphan metabolome features, we connected a lipopeptide of 1,928 Da to an 18-module nonribosomal peptide synthetase encoded as a single gene in chromosome 1. Isolation and structure elucidation led to the identification of selethramide which contains a repeating pattern of serine and leucine and is cyclized at the side chain oxygen of the one threonine residue at position 13. A ( R )-3-hydroxybutyric acid moiety decorates the N -terminal serine. Initial attempts to obtain deletion mutants to probe the role of selethramide failed. After acquiring epigenome (methylome) data for FERM BP-3421, we employed a mimicry by methylation strategy that improved DNA transfer efficiency. Mutants defective in selethramide biosynthesis showed reduced surfactant activity and impaired swarming motility that could be chemically complemented with selethramide. This work unveils a lipopeptide that promotes surface motility, establishes improved DNA transfer efficiency, and sets the stage for continued natural product identification from a prolific strain.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".