Enhancement of fengycin production in <i>Bacillus amyloliquefaciens</i> by genome shuffling and relative gene expression analysis using RT-PCR
Bibliographic record
Abstract
Genome shuffling is an efficient approach for the rapid engineering of microbial strains with desirable industrial phenotypes. In this study, we used genome shuffling in an attempt to improve fengycin production of the wild-type strain Bacillus amyloliquefaciens ES-2-4. After 2 rounds of genome shuffling, a high-yield recombinant F2-72 (FMB72) strain that exhibited 8.30-fold increases in fengycin production was obtained. Comparative analysis of synthetase gene (fenA) expression was conducted between the initial and shuffled strains using fluorescent quantitation RT-PCR. Delta CT (threshold cycle) relative quantitation analysis revealed that fengycin synthetase gene (fenA) expression at the transcriptional level in the FMB72 strain was 12.77-fold greater than in the ES-2-4 wild type. The shuffled strain has a potential application in food and pharmaceutical industries. At the same time, the analysis of improved phenotypes will provide more valuable data for inverse metabolic engineering.
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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) | 0.000 | 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".