Fermentative production of self-toxic fungal secondary metabolites
Bibliographic record
Abstract
Fungi are well known for their vast diversity of secondary metabolites that include many life-saving drugs and highly toxic mycotoxins. In general, fungal cultures producing such metabolites are immune to their toxic effects. However, some are known to produce self-toxic compounds that can pose production optimization challenges if the metabolites are needed in large amounts for chemical modification. One such culture, LV-2841, was identified as the lead for one of our exploratory projects. This culture was found to be a slow grower that produced trace amounts of a known metabolite, cercosporamide, under the standard flask fermentation conditions, and extensive medium optimization studies failed to yield higher titers. Poor growth of the culture in liquid media was attributed to the self-toxicity of cercosporamide to the producing organism, and the minimum inhibitory concentration (MIC) of cercosporamide was estimated to be in the range of 8-16 microg/ml. Fermentations carried out in media containing Diaion HP20 resin afforded significantly higher titers of the desired compound. While several examples of resin-based fermentations of soil streptomyces have been published, this approach has rarely been used for fungal fermentations. Over a 100-fold increase in the production titer of cercosporamide, a self-toxic secondary metabolite, was achieved by supplementing the production medium with a commercially available neutral adsorbent resin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".