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Record W4323350768 · doi:10.1093/jcag/gwac036.048

A48 LYOPHILIZED FECAL MICROBIOTA TRANSPLANT (FMT) DELAYS THE ONSET OF SPONTANEOUS COLITIS IN MUC2-/- MICE

2023· article· en· W4323350768 on OpenAlexaff
Jonathan Chan, Catherine Chan, Ho Pan Sham, Bruce A. Vallance

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsColitisMucin 2MucinMucusMicrobiomeFecesAntibioticsClostridium difficileMedicineGut floraEnterocolitisImmunologyGastroenterologyInflammatory bowel diseaseMetronidazoleMicrobiologyInternal medicineBiologyPathologyDiseaseBioinformatics

Abstract

fetched live from OpenAlex

Abstract Background Fecal microbiota transplant (FMT) is the transfer of fecal microbes from a healthy donor to a recipient to normalize gut microbiota. FMT therapy has been effectively used to prevent recurrent Clostridium difficile infections. While there are emerging studies applying FMT to other gastrointestinal (GI) disorders including inflammatory bowel disease (IBD), its efficacy in treating IBD, and the mechanisms involved remain unclear. In the GI tract, the mucus barrier plays a critical role in protecting the underlying epithelium against harmful luminal stimuli. The secreted mucin 2 (MUC2) is a glycosylated protein and the major component of the GI mucus layer. Muc2 deficient (-/-) mice are used to model IBD because they develop a leaky gut as well as spontaneous colitis. Purpose We investigated the efficacy of FMT in attenuating the development of spontaneous colitis in Muc2-/- mice. This study also evaluated whether FMT using lyophilized stool can successfully alter the gut microbiome of recipient mice. Method To achieve successful colonization of transplanted microbiota, Muc2-/- mice were pretreated prior to FMT with the antifungal amphotericin B, followed by an antibiotic mixture of metronidazole, ampicillin, neomycin and vancomycin in their drinking water for ten days. For two consecutive days, FMT was administered to Muc2-/- mice via consumption of lyophilized stool cakes created from donor C57BL/6 mice, while sucrose cakes were given as control. The fecal microbiome was analyzed at baseline, post-antibiotics and biweekly post-FMT using 16S rRNA sequencing. We monitored body weight and signs of spontaneous colitis, including rectal prolapse, until the mice reached their humane endpoint or turned 30 weeks old. Result(s) Following antibiotic pretreatment, Muc2-/- mice experienced significant weight loss, with 15-20% requiring euthanization within ten days of receiving antibiotics. Unless FMT was administered post-antibiotics, 95% of the mice developed rectal prolapse or otherwise reached their humane endpoint. In comparison, FMT-treated mice rarely developed rectal prolapse, with 55-80% of the FMT-treated mice surviving until the end of the experiment; some FMT-treated mice had no signs of rectal prolapse when euthanized at 30 weeks. Therefore, FMT significantly improved the survival rate of antibiotic-treated Muc2-/- mice and delayed the onset of rectal prolapse / spontaneous colitis. Microbiome analysis revealed that the lyophilized FMT altered the fecal microbiome as compared to mice receiving the sucrose control. Conclusion(s) Notably, FMT increased the survival rates of antibiotic-pretreated Muc2-/- mice while delaying the onset of rectal prolapse. This indicates that lyophilized FMT counteracts the typical spontaneous colitis and weight loss seen in Muc2-/- mice. Our findings can offer clinically relevant insight into how FMT induces a shift in the gut microbiome and its potential usage in treating IBD, as well as other gastrointestinal conditions. Disclosure of Interest None Declared

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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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