Discovery and Characterization of Novel Lignocellulose-Degrading Enzymes from the Porcupine Microbiome
Bibliographic record
Abstract
Abstract Plant cell walls are comprised of cellulose, hemicellulose, and lignin, collectively known as lignocellulose. Microorganisms degrade these components to liberate sugars to meet metabolic demands. Using a metagenomic sequencing approach, we previously demonstrated that the microbiome of the North American porcupine ( Erethizon dorsatum ) is replete with novel lignocellulose-degrading enzymes. Here, we report the identification, synthesis and partial characterization of four genes from the porcupine microbiome encoding putative novel lignocellulose-degrading enzymes, including a β-xylanase, endoxylanase, β-glucosidase, and an ⍺-L-arabinofuranosidase. These genes were identified via conserved catalytic domains associated with cellulose and hemicellulose degradation. We cloned the putative β-xylanase into the pET26b(+) plasmid, enabling inducible gene expression in Escherichia coli ( E. coli ) and periplasmic localization. We demonstrated IPTG-inducible accumulation of β-xylanase protein but failed to detect xylobiose degrading activity in a reporter assay. Alternative assays may be required to measure activity of this putative β-xylanase. In this report, we describe how a synthetic metagenomic pipeline can be used to identify novel microbial lignocellulose-degrading enzymes and take initial steps to introduce a hemicellulose-degradation pathway into E. coli to enable biofuel production from wood pulp feedstock.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".