Identification and Characterization of a New Serratia proteamaculans Strain That Naturally Produces Significant Amount of Extracellular Laccase
Bibliographic record
Abstract
Natural biodegradation processes hold promises for the conversion of agro-industrial lignocellulosic biomaterials into biofuels and fine chemicals through lignin-degrading enzymes. The high cost and low stability of these enzymes remain a significant challenge to economic lignocellulosic biomass conversion. Wood-degrading microorganisms are a great source for novel enzyme discoveries. In this study, the decomposed wood samples were screened, and a promising γ-proteobacterial strain that naturally secreted a significant amount of laccase enzyme was isolated and identified as Serratia proteamaculans AORB19 based on its phenotypic and genotypic characteristics. The laccase activities in culture medium of strain AORB19 were confirmed both qualitatively and quantitatively. Significant cultural parameters for laccase production under submerged conditions were identified following a one-factor-at-a-time (OFAT) methodology: temperature 30°C, pH 9, yeast extract (2 g/l), Li + , Cu 2+ , Ca 2+ , and Mn 2+ (0.5 mM), and acetone (5%). Under the selected conditions, a 6-fold increase (73.3 U/L) in laccase production was achieved when compared with the initial culturing conditions (12.18 U/L). Furthermore, laccase production was enhanced under alkaline and mesophilic growth conditions in the presence of metal ions and organic solvents. The results of the study suggest the promising potential of the identified strain and its enzymes in the valorization of lignocellulosic wastes. Further optimization of culturing conditions to enhance the AORB19 strain laccase secretion, identification and characterization of the purified enzyme, and heterologous expression of the specific enzyme may lead to practical industrial and environmental applications.
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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 | 0.000 |
| 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".