Biosensors for the Detection of Naphthenic Acids in Wastewater from Oil Sands Operations
Bibliographic record
Abstract
Naphthenic acids (NA) are a complex group of acyclic and cyclic alkyl-substituted carboxylic acids that are present in the bitumen mined from the oil sands. NAs accumulate in the tailings waste produced from processing bitumen, are toxic to living organisms and are difficult, both in terms of time and resources, to remediate. In this study, we established a high throughput pipeline using bacterial genomics and synthetic biology methods to build biosensor constructs using promoters from Pseudomonas synxantha, an organism isolated from oilsands process-affected water (OSPW). By observing the gene expression profiles of P. synxantha, we have been able to identify genes that are induced by NAs, and that likely play a role in the transport and catabolism of various NA species. We have identified a catabolic operon that is expressed in a dose-dependent manner, in response to NA exposure. In addition, we identified a TetR regulator that is divergent from this operon, that represses expression of the catabolic operon during normal conditions. The TetR regulator was purified and was shown to bind to the target promoter in electrophoretic mobility shift assays. In the presence of specific naphthenic acids, the repressor loses DNA binding affinity and no longer interacts with the promoter. Based on these findings, we have proposed a model of naphthenic acid sensing through a TetR repressor protein and have therefore identified all the components required to build an NA sensing biosensor using P. synxantha as a chassis. We have therefore identified all the components required to build an NA biosensor using P. synxantha as a chassis. The NA biosensor can be employed as a method to detect NA contamination in the environment, and also to aid in the discovery of novel genes for the purpose of supporting the bioremediation of the 1.2 trillion liters of NA contaminated water currently being stored in Northern Alberta.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".