Effluent decontamination by the ibuprofen-mineralizing strain, Sphingopyxis granuli RW412: Metabolic processes
Bibliographic record
Abstract
The high global consumption of ibuprofen and its limited elimination by wastewater treatment plants (WWTPs), has led to the contamination of aquatic systems by this common analgesic and its metabolites. The potentially negative environmental and public health effects of this emerging contaminant have raised concerns, driving the demand for treatment technologies. The implementation of bacteria which mineralize organic contaminants in biopurification systems used to decontaminate water or directly in processes in WWTPs, is a cheap and sustainable means for complete elimination before release into the environment. In this work, an ibuprofen-mineralizing bacterial strain isolated from sediments of the River Elbe was characterized and assayed to remediate different ibuprofen-polluted media. Strain RW412, which was identified as Sphingopyxis granuli, has a 4.48 Mb genome which includes plasmid sequences which harbor the ipf genes that encode the first steps of ibuprofen mineralization. Here, we confirm that these genes encode enzymes which initiate CoA ligation to ibuprofen, followed by aromatic ring activation by a dioxygenase and retroaldol cleavage to unequivocally produce 4-isobutylcatechol and propionyl-CoA which then undergo further degradation. In liquid mineral salts medium, the strain eliminated more than 2 mM ibuprofen within 74 h with a generation time of 16 h. Upon inoculation into biopurification systems, it eliminated repeated doses of ibuprofen within a few days. Furthermore, in these systems the presence of RW412 avoided the accumulation of ibuprofen metabolites. In ibuprofen-spiked effluent from a municipal WWTP, ibuprofen removal by this strain was 7 times faster than by the indigenous microbiota. These results suggest that this strain can persist and remain active under environmentally relevant conditions, and may be a useful innovation to eliminate this emerging contaminant from urban wastewater treatment systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".