Culturing marine bacteria from the genus <i>Pseudoalteromonas</i> on a cotton scaffold alters secondary metabolite production
Bibliographic record
Abstract
The discovery of secondary metabolites from marine microorganisms is beset by numerous challenges including difficulties cultivating and subsequently eliciting expression of biosynthetic genes from marine microbes in the laboratory. In this paper, we describe a method of culturing three species from the marine bacterial genus Pseudoalteromonas using cotton scaffold supplemented liquid media. This simple cultivation method was designed to mimic the natural behavior of some members of the genus wherein they form epibiotic/symbiotic associations with higher organisms such as sponges and corals or attach to solid structures as a biofilm. Our scaffolded cultivation is highly effective at stimulating an attachment/biofilm phenotype and causes large changes to metabolite profiles for the microbes investigated. Metabolite changes include alteration to the production levels of known molecules such as violacein, thiomarinol A, and the alterochromide and prodiginine families of molecules. Finally and critically, our technique stimulates the production of unknown compounds that will serve as leads for future natural product discovery. These results suggest our cultivation approach could potentially be used as a general strategy for the activation of silent gene clusters in marine microbes to facilitate access to their full natural product biosynthetic capacity.
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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) | 0.001 | 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) | 0.001 | 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".