Microbial degradation of naphthenic acids using constructed wetland treatment systems: metabolic and genomic insights for improved bioremediation of process-affected water
Bibliographic record
Abstract
Naphthenic acids (NAs) are a complex mixture of organic compounds released during bitumen extraction from mined oil sands that are important contaminants of oil sands process-affected water (OSPW). NAs can be toxic to aquatic organisms and, therefore, are a main target compound for OSPW. The ability of microorganisms to degrade NAs can be exploited for bioremediation of OSPW using constructed wetland treatment systems (CWTS), which represent a possible low energy and low-cost option for scalable in situ NA removal. Recent advances in genomics and analytical chemistry have provided insights into a better understanding of the metabolic pathways and genes involved in NA degradation. Here, we discuss the ecology of microbial NA degradation with a focus on CWTS and summarize the current knowledge related to the metabolic pathways and genes used by microorganisms to degrade NAs. Evidence to date suggests that NAs are mostly degraded aerobically through ring cleavage via the beta-oxidation pathway, which can be combined with other steps such as aromatization, alpha-oxidation, omega-oxidation, or activation as coenzyme A (CoA) thioesters. Anaerobic NA degradation has also been reported via the production of benzoyl-CoA as an intermediate and/or through the involvement of methanogens or nitrate, sulfate, and iron reducers. Furthermore, we discuss how genomic, statistical, and modeling tools can assist in the development of improved bioremediation practices.
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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) | 0.000 | 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".