Isolation, identification, and characterisation of the malachite green detoxifying bacterial strain <i>Bacillus pacificus</i> ROC1 and the azoreductase AzrC
Bibliographic record
Abstract
Abstract Malachite green (MG) is used as a dye for materials such as wood, cotton, and nylon, and is used in aquaculture to prevent fungal and protozoan diseases. However, it is highly toxic, with carcinogenic, mutagenic, and teratogenic properties, resulting in bans worldwide. Despite this, MG is still frequently used in many countries due to its efficacy and economy. MG is persistent in the environment and so requires degradative intervention. In this work we isolated Bacillus pacificus ROC1 strain from a salt flat in Pakistan that had the ability to aerobically detoxify MG, as determined by bacterio- and phyto-toxicity assays. We demonstrate immobilized B. pacificus ROC1 can effectively detoxify MG, which highlights a potential method for its biodegradation. Genomic sequencing identified three candidate azo-reductases within B. pacificus ROC1 that could be responsible for the MG-degrading activity. These were cloned, expressed and purified from Escherichia coli , with one (AzrC), catalyzing the reduction of MG to leuco-MG in vitro. AzrC was crystallised and MG was captured within the active site in a Michaelis complex, providing structural insight into the reduction mechanism. Altogether, this work identifies a bacterium capable of aerobically degrading a major industrial pollutant and characterizes the molecular basis for this activity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".