A high throughput screening process and quick isolation of novel lignin-degrading microbes from large number of natural biomasses
Bibliographic record
Abstract
High throughput screening approaches can significantly speed up the identification of novel enzymes from natural microbial consortiums. A two-step high throughput screening process was proposed and explored to screen lignin-degrading microorganisms. By employing this modified culture enrichment method and screening based on enzyme activity, a total of 82 bacterial and 46 fungal strains were isolated from fifty decayed wood samples (100 liquid cultures) collected from the banks of the Ottawa River in Canada. Among them, ten bacterial and five fungal strains were selected and identified based on their high laccase activities by 16S rDNA and ITS gene sequencing, respectively. The study identified bacterial strains from various genera including Serratia, Enterobacter, Raoultella, and Bacillus, along with fungal counterparts including Mucor, Trametes, Conifera, and Aspergillus. Moreover, Aspergillus sydowii (AORF21), Mucor sp. (AORF43), Trametes versicolor (AORF3), and Enterobacter sp. (AORB55) exhibited xylanase and β- glucanase activities in addition to laccase production. The proposed approach allowed for the quick identification of promising consortia and enhanced the chance of isolating desired strains based on desired enzyme activities. This method is not limited to lignocellulose and lignin-degrading microorganisms but can be applied to identify novel microbial strains and enzymes from different natural samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".