Identification of the para-nitrophenol catabolic pathway, and characterization of three enzymes involved in the hydroquinone pathway, in pseudomonas sp. 1-7
Bibliographic record
Abstract
BACKGROUND: para-Nitrophenol (PNP), a priority environmental pollutant, is hazardous to humans and animals. However, the information relating to the PNP degradation pathways and their enzymes remain limited. RESULTS: Pseudomonas sp.1-7 was isolated from methyl parathion (MP)-polluted activated sludge and was shown to degrade PNP. Two different intermediates, hydroquinone (HQ) and 4-nitrocatechol (4-NC) were detected in the catabolism of PNP. This indicated that Pseudomonas sp.1-7 degraded PNP by two different pathways, namely the HQ pathway, and the hydroxyquinol (BT) pathway (also referred to as the 4-NC pathway). A gene cluster (pdcEDGFCBA) was identified in a 10.6 kb DNA fragment of a fosmid library, which cluster encoded the following enzymes involved in PNP degradation: PNP 4-monooxygenase (PdcA), p-benzoquinone (BQ) reductase (PdcB), hydroxyquinol (BT) 1,2-dioxygenase (PdcC), maleylacetate (MA) reductase (PdcF), 4-hydroxymuconic semialdehyde (4-HS) dehydrogenase (PdcG), and hydroquinone (HQ) 1,2-dioxygenase (PdcDE). Four genes (pdcDEFG) were expressed in E. coli and the purified pdcDE, pdcG and pdcF gene products were shown to convert HQ to 4-HS, 4-HS to MA and MA to β-ketoadipate respectively by in vitro activity assays. CONCLUSIONS: The cloning, sequencing, and characterization of these genes along with the functional PNP degradation studies identified 4-NC, HQ, 4-HS, and MA as intermediates in the degradation pathway of PNP by Pseudomonas sp.1-7. This is the first conclusive report for both 4-NC and HQ- mediated degradation of PNP by one microorganism.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".