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Record W4407284935 · doi:10.1093/jcag/gwae059.082

A82 DIETARY PROTEIN COMPOSITION ALTERS INTESTINAL INFLAMMATION AND MTOR ACTIVATION

2025· article· en· W4407284935 on OpenAlexaff
L Rondeau, Pranshu Muppidi, Boaz Luz, Kazuomi Kan, Rammy Dang, X Wang, Alberto Caminero

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInflammationPI3K/AKT/mTOR pathwayComposition (language)ChemistryCell biologyBiologyInternal medicineMedicineBiochemistrySignal transductionArt

Abstract

fetched live from OpenAlex

Abstract Background Environmental factors, notably changes in diet and microbiota composition, have been identified as key contributors to the development of inflammatory bowel diseases (IBD). The prevalence of IBD is on the rise, particularly in industrialized nations with populations consuming western-style diets rich in fat and protein. While total and animal protein intake have been associated with IBD, their specific impact on intestinal inflammation remains poorly understood. Branch-chain amino acids found in different protein source possess bioactive properties and can interact with the mechanistic target of rapamycin (mTOR), a cellular growth regulator that controls autophagy and inflammation. Given the observed mTOR hyperactivation in IBD patients, coupled with its correlation with exacerbated colitis severity in murine models, a comprehensive exploration into the relationship between dietary protein intake, mTOR activation, autophagy, and intestinal inflammation is warranted. Aims To study the influence of high protein and animal protein diets on mTOR activation, autophagy, and intestinal inflammation in mouse models of colitis. Methods Specific pathogen free C57BL/6 mice were fed a high protein diet (HPD; 40% casein), animal meat protein diet (APD; 14% mixed protein), or control diet (CD; 14% casein), for three weeks prior to tissue collection (N=6 per diet) or experimental colitis induction (N=8 per diet). Experimental colitis was induced using dextran sulfate sodium in drinking water (DSS; 2.5% w/v) or colonic administration of 2,4,6-trinitrobenzene sulfonic acid (TNBS; 2%). DSS was provided ad libitum for five days, followed by two days of water recovery before tissue collection. Rapamycin (10 mg/kg/day) was provided intraperitoneally to a subset of mice as an mTOR inhibitor. mTOR activation and autophagy markers were assessed by western blot (WB) of mTOR substrates and autophagy proteins in colon tissue extracts pre- and post-colitis. Apoptotic cells in colon cross sections were quantified by TUNEL. Susceptibility to colitis was assessed by histologic analysis of distal colon sections, weight loss, and clinical scores. Inflammatory gene transcripts were quantified in colon tissue by Nanostring. Results DSS and TNBS exposure in mice fed the HPD and APD led to greater weight loss, tissue damage, inflammatory gene signaling, diarrhea, and stool blood. HPD and APD increased mTOR activation (phosphorylation of mTOR and S6 ribosomal kinase) compared to CD. Autophagy proteins were downregulated, and apoptotic cells were increased in intestinal tissue of HPD- and APD-fed mice, before colitis. Inhibition of mTOR with rapamycin restored autophagy and reduced colitis severity. Conclusions Protein quantity, source, and BCAA content optimization is crucial for determining inflammation, mTOR activation, and autophagy in mouse models of colitis. Funding Agencies CAG, CCC, CIHR

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.211
Teacher spread0.207 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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