Implementation of an adaptive laboratory evolution strategy for improved production of valuable microbial secondary metabolites
Bibliographic record
Abstract
Microbial surface-active agents, such as rhamnolipids, represent an attractive substitute for synthetic surfactants. However, current production bioprocesses are generally inefficient. Adaptive laboratory evolution strategies could offer a promising avenue to improve secondary metabolites production. In the bacterium Burkholderia thailandensis, the social behaviour called swarming motility relies on biosynthesis of rhamnolipids. Since experimental swarming requires lower agar concentrations, we hypothesized that augmenting the agar concentration would constrain the cells to produce more rhamnolipids. Consecutive rounds of B. thailandensis cultivation on swarming media performed with increasing agar concentrations enhanced rhamnolipid production by the evolved populations, with a correlation between rhamnolipid production and agar concentrations. Whole-genome sequencing of superior producing evoluants revealed inactivating mutations in qsmR, which codes for a transcriptional regulator not known to influence rhamnolipid production. Results indicate that QsmR represses rhamnolipid biosynthetic genes transcription. The developed directed evolution strategy could be used to improve biosurfactant yields with other producing bacteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".