Functional analysis of the <i>ocnE</i> gene involved in nicotine-degradation pathways in <i>Ochrobactrum intermedium</i> SCUEC4 and its enzymatic properties
Bibliographic record
Abstract
The SCUEC4 strain of Ochrobactrum intermedium is a newly isolated bacterium that degrades nicotine can use nicotine as the sole carbon source via a series of enzymatic catalytic processes. The mechanisms underlying nicotine degradation in this bacterium and the corresponding functional genes remain unclear. Here, we analyzed the function and biological properties of the ocnE gene involved in the nicotine-degradation pathways in strain SCUEC4. The ocnE gene was cloned by PCR with total DNA of strain SCUEC4 and used to construct the recombinant plasmid pET28a-ocnE. The overexpression of the OcnE protein was detected by SDS–PAGE analysis, and study of the function of this protein was spectrophotometrically carried out by monitoring the changes of 2,5-dihydroxypyridine. Moreover, the effects of temperature, pH, and metal ions on the biological activities of the OcnE protein were analyzed. The optimal conditions for the biological activities of OcnE, a protein of approximately 37.6 kDa, were determined to be 25 °C, pH 7.0, and 25 μmol/L Fe2+, and the suitable storage conditions for the OcnE protein were 0 °C and pH 7.0. In conclusion, the ocnE gene is responsible for the ability of 2,5-dihydroxypyridine dioxygenase. These findings will be beneficial in clarifying the mechanisms of nicotine degradation in O. intermedium SCUEC4.
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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 | 0.000 | 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".