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Record W4321106423 · doi:10.3390/molecules28041881

Characterization of the Glucan-Branching Enzyme GlgB Gene from Swine Intestinal Bacteria

2023· article· en· W4321106423 on OpenAlexaff
Yuqi Shao, W. Wang, Ying Hu, Michael G. Gänzle

Bibliographic record

VenueMolecules · 2023
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAmyloseResistant starchStarchPolysaccharideGlucanBiochemistryFood scienceChemistryEnzymeHydrolysisFermentationFirmicutesOligosaccharideGene

Abstract

fetched live from OpenAlex

Starch hydrolysis by gut microbiota involves a diverse range of different enzymatic activities. Glucan-branching enzyme GlgB was identified as the most abundant glycosidase in Firmicutes in the swine intestine. GlgB converts α-(1→4)-linked amylose to form α-(1→4,6) branching points. This study aimed to characterize GlgB cloned from a swine intestinal metagenome and to investigate its potential role in formation of α-(1→4,6)-branched α-glucans from starch. The branching activity of purified GlgB was determined with six different starches and pure amylose by quantification of amylose after treatment. GlgB reduced the amylose content of all 6 starches and amylose by more than 85% and displayed a higher preference towards amylose. The observed activity on raw starch indicated a potential role in the primary starch degradation in the large intestine as an enzyme that solubilizes amylose. The oligosaccharide profile showed an increased concentration of oligosaccharide introduced by GlgB that is not hydrolyzed by intestinal enzymes. This corresponded to a reduced in vitro starch digestibility when compared to untreated starch. The study improves our understanding of colonic starch fermentation and may allow starch conversion to produce food products with reduced digestibility and improved quality.

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 score0.089
Threshold uncertainty score0.267

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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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