Effect of growth conditions on the production of manganese peroxidase by three strains of <i>Bjerkandera adusta</i>
Bibliographic record
Abstract
We were looking for a strain of Bjerkandera adusta that produces high titres of manganese peroxidase under optimal conditions for large-scale enzyme purification. We have chosen two strains from the University of Alberta Microfungus Collection and Herbarium, UAMH 7308 and 8258, and compared the effects of growth conditions and medium composition on enzyme production with the well-characterized strain BOS55 (ATCC 90940). Of four types of cereal bran examined, rice bran at 3% (w/v) in 60 mM phosphate buffer pH 6 supported the highest levels of enzyme production. Using 100 mL medium in 500-mL Erlenmeyer flasks, maximum enzyme levels in the culture supernatant occurred after about 10 days of growth; 5.5 U x mL(-1) for UAMH 7308, 4.4 U x mL(-1) for UAMH 8258, and 1.7 U x mL(-1) for BOS55, where units are expressed as micromoles of Mn-malonate formed per minute. Growth as submerged cultures in 10-L stirred tank reactors produced 3.5 U x mL(-1) of manganese peroxidase (MnP) by UAMH 8258 and 2.5 U x mL(-1) of MnP by 7308, while enzyme production by BOS55 was not successful in stirred tank reactors but could be scaled up in 2-L shake flasks containing 400 mL rice bran or glucose-malt-yeast extract (GMY)-Mn-glycolate medium to produce MnP levels of 1.7 U x mL(-1). These results show that the two strains of B. adusta, UAMH 7308 and 8258, can produce between two and three times the manganese peroxidase level of B. adusta BOS55, that they are good candidates for scale up of enzyme production, and that the rice bran medium supports higher levels of enzyme production than most previously described media.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".