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Record W2907096890 · doi:10.1371/journal.pone.0209221

Characterization of novel lignocellulose-degrading enzymes from the porcupine microbiome using synthetic metagenomics

2019· article· en· W2907096890 on OpenAlexafffund
Mackenzie Thornbury, Jacob Sicheri, Patrick D. Slaine, Landon J. Getz, Emma Finlayson‐Trick, Jamie Cook, Caroline Guinard, Nicholas Boudreau, David L. Jakeman, John R. Rohde, Craig McCormick

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsDalhousie University
FundersDalhousie UniversityDalhousie Medical Research Foundation
KeywordsMetagenomicsMicrobiomeComputational biologyBiologyBioinformaticsBiochemistryGene

Abstract

fetched live from OpenAlex

Plant cell walls are composed of cellulose, hemicellulose, and lignin, collectively known as lignocellulose. Microorganisms degrade lignocellulose 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 genes that could encode lignocellulose-degrading enzymes. Here, we report the identification, synthesis and partial characterization of four novel genes from the porcupine microbiome encoding putative lignocellulose-degrading enzymes: β-glucosidase, α-L-arabinofuranosidase, β-xylosidase, and endo-1,4-β-xylanase. These genes were identified via conserved catalytic domains associated with cellulose- and hemicellulose-degradation. Phylogenetic trees were created for each of these putative enzymes to depict genetic relatedness to known enzymes. Candidate genes were synthesized and cloned into plasmid expression vectors for inducible protein expression and secretion. The putative β-glucosidase fusion protein was efficiently secreted but did not permit Escherichia coli (E. coli) to use cellobiose as a sole carbon source, nor did the affinity purified enzyme cleave p-Nitrophenyl β-D-glucopyranoside (p-NPG) substrate in vitro over a range of physiological pH levels (pH 5-7). The putative hemicellulose-degrading β-xylosidase and α-L-arabinofuranosidase enzymes also lacked in vitro enzyme activity, but the affinity purified endo-1,4-β-xylanase protein cleaved a 6-chloro-4-methylumbelliferyl xylobioside substrate in acidic and neutral conditions, with maximal activity at pH 7. At this optimal pH, KM, Vmax, and kcat were determined to be 32.005 ± 4.72 μM, 1.16x10-5 ± 3.55x10-7 M/s, and 94.72 s-1, respectively. Thus, our pipeline enabled successful identification and characterization of a novel hemicellulose-degrading enzyme from the porcupine microbiome. Progress towards the goal of introducing a complete lignocellulose-degradation pathway into E. coli will be accelerated by combining synthetic metagenomic approaches with functional metagenomic library screening, which can identify novel enzymes unrelated to those found in available databases.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.182
Teacher spread0.145 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2019
Admission routes2
Has abstractyes

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