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Record W4323352978 · doi:10.1093/jcag/gwac036.267

A267 IDENTIFYING ENTEROBACTERIACEAE VIRULENCE GENES ASSOCIATED WITH ACTIVE DISEASE IN ULCERATIVE COLITIS PATIENTS USING CULTURE-DEPENDENT AND -INDEPENDENT APPROACHES

2023· article· en· W4323352978 on OpenAlexaffabout
Dominique Tertigas, Firas Rinawi, A Griffiths, Matthew D. Surette

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsSickKids FoundationUniversity of TorontoMcMaster University
Fundersnot available
KeywordsVirulenceEnterobacteriaceaeUlcerative colitisMicrobiologyBiologyMicrobiomeInflammatory bowel diseaseMacConkey agarEscherichia coliColitisFecesGut floraGeneImmunologyDiseaseGeneticsMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background The prevalence of inflammatory bowel disease (IBD) in Canada is among the highest in the world and is estimated to affect 1 in 100 Canadians by 2030. Ulcerative colitis (UC) is a type of IBD characterized by mucosal inflammation of the large intestine. UC is believed to arise through a complex interplay of the host immune responses and changes in the gut microbiota in a genetically susceptible individual. Therapies targeting the gut microbiota, such as antibiotics and fecal microbiota transplantation (FMT), have been effective in treating UC, suggesting infectious triggers should be explored. Purpose Some data suggests the development of UC can be driven by pathogenic bacteria of the family Enterobacteriaceae, which can carry virulence genes important for colonizing the gut (e.g. fimH) and disrupting the intestinal epithelium (e.g. hylA). However, many studies have focused on a single species (e.g. Escherichia coli) and thereby underestimate the importance of these virulence genes that are shared across the Enterobacteriaceae family. I aim to investigate whether specific virulence genes contribute to disease activity in some patients with UC and to show that these virulence genes are carried by strains of many Enterobacteriaceae species. Method UC patient stool samples were collected throughout enrolment in randomized control trials of FMT for adult UC and microbiome studies in early-onset pediatric UC. The stool samples were cultured on MacConkey agar to enrich for Enterobacteriaceae. Samples from before and after treatment were sent for targetted cultured-enriched metagenomic sequencing and strains were isolated from baseline samples only for whole genome sequencing. The taxonomy of each genome and taxonomic composition of each metagenome were annotated along with virulence genes and antimicrobial resistance genes. Phenotypic assays of cultured isolates were used to capture diversity and virulence activity. Result(s) Approximately 7500 colonies from UC patient stool samples were isolated and phenotyped. Based on the initial screens, 130 isolates were selected to comprise our Enterobacteriaceae strain collection. Across all patient samples, we detected 19 different species of the Enterobacteriaceae family across six genera from the genomic and metagenomic data. We identified virulence genes found across multiple species from the Enterobacteriaceae family within genomes and metagenomes, and by performing phenotypic assays of the cultured isolates. Conclusion(s) Further exploration of the distribution of these virulence genes in UC patients during active disease or remission and healthy controls can provide insight into the pathogenesis of UC. Identifying infectious agents in even a subset of UC patients will allow for more targeted diagnosis and treatment approaches. 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)0.001
metaresearch head score (Gemma)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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
Published2023
Admission routes2
Has abstractyes

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