Degradation of sulphonated mono and di-azo dye as the sole carbon source in <i>Serratia marcescens</i> : Insights from combined wet and dry lab analysis
Bibliographic record
Abstract
Abstract The high production volume of azo dyes for manufacturing and treating various consumer products leads to deleterious environmental consequences. Bacterial agents present in the environment can degrade these dyes. We, hereby, report the isolation, decolourization and degradation of a mono (Methyl orange) and di-azoic (Congo red) compound of this class of dyes by a versatile bacterium Serratia marcescens . Our isolate showed the capability of sulphonated azo dye utilization/degradation i.e. Methyl orange and Congo red usage, with no inhibitory effects on its growth in minimal medium. The calorimetric analysis showed 80.83% decolourization of Methyl orange and 92.7% decolourization of Congo red after 7 days of incubation in a shaking incubator at pH: 7 and temperature: 37 °C. An azoreductase enzyme of ∼25 KDa was detected after SDS-PAGE analysis. Quantitative and qualitative testing of the degradation phenomenon was followed by in silico analysis. Structural modeling followed by molecular docking in Molecular Operating Environment revealed numerous residues involved in binding and assisting degradation. Changes in the apo, holo, and dye-bound enzyme energy profiles were also observed. This is the first study reporting the capability of Serratia marcescens to use azo dyes/sulphonated azo dyes as the sole carbon source and the detailed computational analysis of the degradation phenomenon. We hope that these findings will be of use to environmental scientists, aid in better dye-degrading mutant creation to help craft future remediation strategies for sulphonated azo dyes.
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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".