Morphology engineering of <i>Aspergillus oryzae</i> for <scp>l</scp>‐malate production
Bibliographic record
Abstract
Abstract Aspergillus oryzae is a competitive natural producer for organic acids, but its production capacity is closely correlated with a specific morphological type. Here, morphology engineering was used for tailoring A. oryzae morphology to enhance l‐malate production. Specifically, correlation between A. oryzae morphology and l‐malate fermentation was first conducted, and the optimal range of the total volume of pellets in a unit volume of fermentation broth (V value) for l‐malate production was 120–130 mm3/ml. To achieve this range, A. oryzae morphology was improved by controlling the variation of operational parameters, such as agitation speed and aeration rate, and engineered by optimizing the expression of cell division cycle proteins such as tyrosine‐protein phosphatase (CDC14), anaphase promoting complex/cyclosome activator protein (CDC20), and cell division control protein 45 (CDC45). By controlling the strength of CDC14 at a medium level, V value fell into the optimal range of V value and the final engineered strain A. oryzae CDC14(3) produced up to 142.5 g/L l‐malate in a 30‐L fermenter. This strategy described here lays a good foundation for industrial production of l‐malate in the future, and opens a window to develop filamentous fungi as cell factories for production of other chemicals.
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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) | 0.000 | 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".