Does Osmotic Stress Affect Natural Product Expression in Fungi?
Bibliographic record
Abstract
The discovery of new natural products from fungi isolated from the marine environment has increased dramatically over the last few decades, leading to the identification of over 1000 new metabolites. However, most of the reported marine-derived species appear to be terrestrial in origin yet at the same time, facultatively halo- or osmotolerant. An unanswered question regarding the apparent chemical productivity of marine-derived fungi is whether the common practice of fermenting strains in seawater contributes to enhanced secondary metabolism? To answer this question, a terrestrial isolate of Aspergillus aculeatus was fermented in osmotic and saline stress conditions in parallel across multiple sites. The ex-type strain of A. aculeatus was obtained from three different culture collections. Site-to-site variations in metabolite expression were observed, suggesting that subculturing of the same strain and subtle variations in experimental protocols can have pronounced effects upon metabolite expression. Replicated experiments at individual sites indicated that secondary metabolite production was divergent between osmotic and saline treatments. Titers of some metabolites increased or decreased in response to increasing osmolite (salt or glycerol) concentrations. Furthermore, in some cases, the expression of some secondary metabolites in relation to osmotic and saline stress was attributed to specific sources of the ex-type strains.
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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) | 0.001 | 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".