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Intestinal microbiota differentially affect brush border enzyme activity and gene expression in the neonatal gnotobiotic pig

2008· article· en· W2162770602 on OpenAlexafffund
Benjamin P. Willing, Andrew G. Van Kessel

Bibliographic record

VenueJournal of Animal Physiology and Animal Nutrition · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsBrush borderBiologyGene expressionAffect (linguistics)GeneEnzymeDigestive enzymeMicrobiologyEnzyme assayCell biologyBiochemistryAmylaseCommunication

Abstract

fetched live from OpenAlex

To study microbial influence on intestinal development pertaining to nutrient digestion, two separate gnotobiotic experiments were performed, each with 16 piglets allocated to four treatment groups: germfree (GF), monoassociation with Escherichia coli, monoassociation with Lactobacillus fermentum or conventionalization with faecal bacteria (CV). Enzyme activity and gene expression of lactase phlorizin hydrolase (LPH) and aminopeptidase N (APN) were measured in isolated enterocytes, harvested on day 14, using specific substrates and quantitative PCR respectively. Enterocytes of CV pigs had reduced APN activity, but had increased gene expression relative to GF, making the specific activity:mRNA (A:G) ratio dramatically lower (p < 0.05). Similarly, LPH A:G ratio was significantly reduced (p < 0.05) in enterocytes of CV pigs as compared with GF. The results of co-incubation of L. fermentum, E. coli and faecal bacteria with APN indicate a direct relationship between enzyme inactivation and specific A:G ratio in enterocytes. We conclude that enterocyte up-regulation of APN expression occurs as either a direct response to microbial colonization or as a feedback mechanism in response to reduced enzyme activity through microbial degradation. This mechanism may play a role in ensuring effective competition of the host with the intestinal microbiota for available nutrients.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.467

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 head0.011
GPT teacher head0.263
Teacher spread0.251 · 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

Citations64
Published2008
Admission routes2
Has abstractyes

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