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Record W3134048592 · doi:10.1093/jcag/gwab002.022

A23 DIETARY PROTEIN AND AMINO ACID COMPOSITIONS INFLUENCE MICROBIOTA, INTESTINAL PERMEABILITY, AND SUSCEPTIBILITY TO COLITIS

2021· article· en· W3134048592 on OpenAlexaffabout
L Rondeau, J Godbout, X Wang, A Caminero Fernandez

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsColitisGut floraInternal medicineAmino acidBiologyCaseinDysbiosisFecesIntestinal permeabilityGastroenterologyFood scienceImmunologyMedicineBiochemistryMicrobiology

Abstract

fetched live from OpenAlex

Abstract Background Environmental factors, such as alterations in diet and microbiota, have been linked to inflammatory bowel diseases (IBD). The incidence of IBD is rising, particularly in Canada and other industrialized nations that consume western-style diets high in fat and protein. While most dietary proteins and amino acids are absorbed in the small intestine, substantial amounts can enter the colon for microbial metabolism and to exert effects on intestinal tissue and immune cells. Prospective cohort studies suggest that diets high in protein are associated with an increased risk of IBD. However, the role of excess dietary protein and amino acids in IBD pathogenesis is not clear. Aims To study whether and how consumption of diets high in protein or amino acids influences intestinal inflammation, colitis severity, and intestinal microbiota. Methods To assess the influence of dietary protein composition on colitis severity, specific pathogen-free C57BL/6 mice were fed isocaloric casein-based purified diets containing low (7%), normal (14%), or high (35%) protein (HPD). Mice were also fed an amino acid-defined diet (AAD) with amino acid and ingredient composition matched to the normal protein diet. Following three weeks of diet consumption ad libitum, mice were continued on the same diet and mucosal injury was induced with 2% dextran sulfate sodium (DSS; 5 days) followed by water (2 days) before sacrifice. Mice were monitored daily for clinical signs of colitis. Susceptibility to colitis was assessed by analysing stool consistency and blood, microscopic scoring (Cooper score), and by immunohistochemistry of colon tissue. Fecal microbiota (16S rRNA Illumina), intestinal permeability (Ussing chambers), proinflammatory gene expression (NanoString and RT-qPCR), and bacterial translocation (plating) were analysed. Results Following DSS exposure, mice fed HPD and AAD experienced greater weight loss, bacterial translocation to the spleen, stool blood, and diarrhea compared to mice fed the normal protein control diet. While all DSS-treated mice developed colitis, HPD and AAD fed mice also developed greater histologic damage, intestinal permeability, and innate immune cell infiltration. Cytokine profiling revealed that AAD is associated with significant up-regulation of IL-18 during colitis. Principle coordinates analysis based on Bray-Curtis dissimilarities demonstrates distinct shifts in the fecal microbiota of mice fed HPD and AAD. Conclusions These results suggest that excess dietary protein and amino acids are associated with more severe colitis and microbiota alterations in the DSS model. Previous studies demonstrate that IL-18 is up-regulated in IBD patients. Its overexpression may incite inflammation by stimulating cytokine signalling through NFκB and modify microbial community structure by regulating antimicrobial peptides. Funding Agencies CIHRFarncombe Family Digestive Health Research Institute, Douglas Family Chair in Gastroenterology Research

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2021
Admission routes2
Has abstractyes

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