Evolutionary adaptation of <i>Aspergillus niger</i> for increased ferulic acid tolerance
Bibliographic record
Abstract
AIMS: To create an Aspergillus niger mutant with increased tolerance against ferulic acid using evolutionary adaptation. METHODS AND RESULTS: ferulic acid. The A. niger mutant obtained after six months, named Fa6, showed increased ferulic acid tolerance compared to the parent. In addition, Fa6 has increased ferulic acid consumption and a higher conversion rate, suggesting that the mutation affects aromatic metabolism of this species. Transcriptome analysis of the evolutionary mutant on ferulic acid revealed a distinct gene expression profile compared to the wild type. Further analysis of this mutant and the parent strain provided the first experimental confirmation that A. niger converts coniferyl alcohol to ferulic acid. CONCLUSIONS: The evolutionary adaptive A. niger mutant Fa6 has beneficial mutations that increase the tolerance, conversion rate and uptake of ferulic acid. SIGNIFICANCE AND IMPACT OF THE STUDY: This study demonstrates that evolutionary adaptation is a powerful tool to modify micro-organisms towards increased tolerance to harsh conditions, which is beneficial for various industrial applications.
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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 | 0.001 |
| 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".