Hub Metabolites Promote the Bioflocculant Production in a Biomass-Degrading Bacterium Pseudomonas boreopolis GO2
Bibliographic record
Abstract
INTRODUCTION: The low yield of bioflocculants has been a bottleneck problem that limits their industrial applications. Understanding the metabolic mechanism of bacteria that produce bioflocculants could provide valuable insights and strategies to directly regulate their yield in future. METHODS: To investigate the change of metabolites in the process of bioflocculant production by a biomass-degrading bacterium, Pseudomonas boreopolis GO2, an untargeted metabolome analysis was performed. RESULTS: The results showed that metabolites significantly differed during the fermentation process when corn stover was used as the sole carbon source. The differential metabolites were divided into four co-expression modules based on the weighted gene co-expression network analysis. Among them, a module (yellow module) was closely related to the flocculating efficiency, and the metabolites in this module were mainly involved in carbohydrate, lipid, and amino acid metabolism. The top 30 metabolites with the highest degree in the yellow module were identified as hub metabolites for bioflocculant production. Finally, 10 hub metabolites were selected to perform the additional experiments, and the addition of L-rhamnose, tyramine, tryptophan, and glutaric acid alone all could significantly improve the flocculating efficiency of GO2 strain. CONCLUSION: These results indicated that the hub metabolites were key for bioflocculant production in GO2 strain, and could help guide the improvement of high-efficiency and low-cost bioflocculant production.
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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) | 0.001 | 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".