Quorum Sensing Controls Both Rhamnolipid and Polyhydroxyalkanoate Production in Burkholderia thailandensis Through ScmR Regulation
Bibliographic record
Abstract
Rhamnolipids are surface-active agents of microbial origin used as alternatives to synthetic surfactants. Burkholderia thailandensis is a non-pathogenic rhamnolipid-producing bacterium that could represent an interesting candidate for use in commercial processes. However, current bioprocesses for rhamnolipid production by this bacterium are not efficient enough, mainly due to low yields. Since regulation of rhamnolipid biosynthesis in B. thailandensis remains poorly understood, identifying new regulatory factors could help increase the production of these valuable metabolites. We performed a random transposon mutagenesis screening to identify genes directing rhamnolipid production in B. thailandensis E264. The most efficient rhamnolipid producer we identified harbored an inactivating transposon insertion in the scmR gene, which was recently described to encode as a secondary metabolite regulator in B. thailandensis. We investigated the impact of scmR loss on rhamnolipid biosynthesis and cell growth. Because biosynthesis of rhamnolipids and polyhydroxyalkanoates (PHAs) could share the same pool of lipid precursors, we also investigate the effect of ScmR on PHA production. We found that production of both rhamnolipids and PHAs are modulated by ScmR during the logarithmic growth phase and demonstrate that ScmR downregulates the production of rhamnolipids by affecting the expression of both rhl biosynthetic operons. Furthermore, our results indicate that PHA biosynthesis is reduced in the scmR- mutant, asScmR promotes the transcription of the phaC and phaZ genes. By studying the relationship between ScmR and quorum sensing (QS) regulation we reveal that QS acts as an activator of scmR transcription. Finally, we pinpoint the QS-3 system as being involved in the regulation of rhamnolipid and PHA biosynthesis. We conclude that ScmR negatively affects rhamnolipid production, whereas it positively impacts PHAs biosynthesis. This could provide an interesting approach for future strain engineering, leading to improved yields of these valuable metabolites.
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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) | 0.000 | 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) | 0.001 | 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".