Quorum sensing involvement in response surface methodology for optimisation of sclerotiorin production by Penicillium sclerotiorum in shaken flasks and bioreactors
Bibliographic record
Abstract
Abstract Purpose Sclerotiorin, an azaphilone produced by some filamentous fungi including Penicillium sclerotiorum, is a pigment with variety of biological activities including lipoxygenase inhibition, reduction of cholesterol levels, and anti-cancer properties. Sclerotiorin has potential use in pharmaceutical as well as food industries. In this context, the purpose of this study was to provide a simple and robust procedure for optimised production of sclerotiorin by P. sclerotiorum using a central composite design developed through response surface methodology (RSM) and to identify the molecule(s) involved in the signalling mechanism in P. sclerotiorum. Methods The optimisation of sclerotiorin production was carried out using RSM in shaken flasks and the obtained results were then replicated using a 2-L stirred tank bioreactor. Penicillium sclerotiorum ethyl acetate culture extract was analysed using thin layer chromatography (TLC) and potential signalling molecules were identified using Gas chromatography-mass spectrometry (GC-MS). Results The experimental studies suggested an increase in the sclerotiorin production by 2.1-fold and 2.2-fold in shaken flasks and stirred tank bioreactors respectively. Further analysis of P. sclerotiorum ethyl acetate culture extract reported the presence of ricinoleic acid, an oxylipin, belonging to a family of signalling molecules tentatively involved in the enhancement of sclerotiorin production. Conclusion This paper has highlighted the positive effect of the optimal supplementation of P. sclerotiorum culture extracts for enhanced production of sclerotiorin. It has also examined potential molecules involved in the signalling mechanism in P. sclerotiorum culture extract for the overproduction of sclerotiorin.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".