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Record W2791008915 · doi:10.1093/jcag/gwy008.012

A11 EFFECT OF FECAL MICROBIAL TRANSPLANT ON MICROBIAL AND PHAGE COMPOSITION IN PATIENTS WITH CLOSTRIDIUM DIFFICILE INFECTION

2018· article· en· W2791008915 on OpenAlexaffabout
H Park, Braden Millan, Naomi Hotte, Dina Kao, Karen Madsen

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMicrobiologyBiologyClostridium difficileMetagenomicsSiphoviridaeBacteroidesEscherichia coliMicrobiomePhage therapyBacteriophageGeneticsGeneBacteriaAntibiotics

Abstract

fetched live from OpenAlex

The gut microbiome contains a diverse bacteriophage community that plays a largely unknown role in shaping microbial colonization and disease pathogenesis. Fecal microbial transplantation (FMT) is the most effective therapy for recurrent Clostridium difficile infection (RCDI) and has been shown to transfer phages along with gut microbes. The aim of this study was to examine the effects of FMT on microbial and phage composition in RCDI patients. Patients with RCDI (n=19) received FMT from 1 of 3 donors via colonoscopy. Stool samples were collected prior to and following FMT. DNA was extracted and indexed paired-end DNA libraries constructed using an Illumina Nextera® XT DNA kit, then sequenced on a MiSeq. Reads from individual samples were mapped to >5 kb assembled contigs using Metaphlan 2 for taxonomy, HUMAnN for gene function, and Bowtie2 with NCBI RefSeq database for prophage. To assess the metabolic state of the microbial community in RDCI patients, growth dynamics of E. coli were inferred from the metagenomic data by measuring the proportion of DNA copies near the origin to those near the terminus (peak-to-trough ratio (PTR)). In RCDI patients prior to FMT, Escherichia and Klebsiella dominated. RCDI patients also harbored numerous phages within the Siphoviridae family, including Enterobacteria, Escherichia, Salmonella, Klebsiella and Lactobacillus phages. In contrast, the gut microbiome of donors consisted primarily of Bacteroides and Firmicutes; donors also had a much reduced phage population which consisted primarily of crAssphage, a phage predicted to infect Bacteroides. Eleven patients were successfully treated with a single FMT (FMT-S) while 8 patients required multiple FMTs (FMT-M). A successful FMT resulted in the appearance of crAssphage in the RCDI recipients with a complete loss or reduction of Siphoviridae phages and increased Bacteroidetes and Firmicutes. There were no significant differences in microbial composition or predicted gene function between the FMT-S and FMT-M groups; however, the patients requiring multiple FMT had increased abundances of Enterobacteria phages HK542, mEp237, and phiP27. This was associated with an increased inferred growth rate of Escherichia coli suggesting that E. coli and associated phages may be driving disease pathogenesis in some RCDI patients that fail to respond to FMT. RCDI patients had decreased microbial but increased diversity of phages compared with healthy individuals. FMT altered both bacterial and phage composition to resemble the donor. Patients who required at least 2 FMT had significant differences in their phage population suggesting that the presence of particular phages may have a role in modulating response of patients to fecal transplantation. CIHRAlberta Health Services; Alberta Innovates

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.002
GPT teacher head0.188
Teacher spread0.186 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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