Bioinformatic evaluation of the secondary metabolism of antistaphylococcal environmental bacterial isolates
Bibliographic record
Abstract
The increasing occurrence of drug-resistant Staphylococcus aureus is exacerbated with a declining rate of antibiotic discovery, particularly those with new mechanisms of action. The decline in antibiotic discovery from traditional sources, such as soil actinobacteria, necessitates examination of lesser studied microbes. Here, we present a strategy to select for organisms that may have a propensity to result in new antistaphylococcal agents by using S. aureus as a bait organism, and selecting organisms that have a natural lytic activity towards it. We have isolated over 80 environmental isolates and typed these organisms using 16S rDNA sequence comparison and deployed bioinformatics to assess the secondary metabolic potential of the isolated antistaphylococcal bacteria using genomic sequences. Bioinformatic analysis highlights the enriched and unique suite of potential antibiotic polyketides and nonribosomal peptides and lantibiotic gene clusters from these organisms. Profiling organic microbial extracts further showed that many of the organisms from the 10 staphylolytic genera secrete agents with antistaphylococcal activity and may serve as new sources for future antistaphylococcal drug discovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".