Revealing degradation strategy of aniline blue by <i>Lysinibacillus</i> sp. 38-6 via genomic analysis
Bibliographic record
Abstract
Aniline blue, a triphenylmethane dye, has been widely used in industrial and medical fields, leading to its gradual enrichment in environmental water. Its removal and degradation from the water is essential but very challenging. A bacterium Lysinibacillus sp. 38-6, which is able to efficiently degrade aniline blue, was isolated from the surface soil samples under withered leaves. The strain exhibited excellent decolorization capacity at high concentrations of aniline blue (91% at 1000 mg/L) and salt (90% at 75 g/L) as well as high temperature (92% at 45 °C). To investigate the dye degradation strategies of Lysinibacillus sp. 38-6 at the genomic level, its genome was sequenced and analyzed. The isolate possesses abundant genomic features responsible for degrading dyes. In particular, several genes encoding laccase, iron-dependent peroxidase, NAD(P)H-dependent FMN reductase, and short-chain dehydrogenase/reductases might contribute to the cleavage of chromophore groups and aromatic rings in aniline blue. In addition, a number of genes required for heat, salt, and oxidative stress responses were found, indicating that Lysinibacillus sp. 38-6 is able to efficiently degrade dyes at higher temperature, salty and oxidative environments. It is thus inferred that isolate 38-6 has the potential for applications in efficient degradation of aniline blue in wastewater treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".