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Record W4323350809 · doi:10.1093/jcag/gwac036.040

A40 GUT MICROBIOTA PROMOTES NUTRIENT AVAILABLITY AND PATHOGENESIS OF AN ATTACHING/ EFFACING BACTERIAL PATHOGEN

2023· article· en· W4323350809 on OpenAlexafffund
Qiuli Liang, Jean M. Allaire, Larissa Sbaglia Celiberto, Hongbing Yu, Bruce A. Vallance

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMicrobiologyPathogenBiologyVirulenceCitrobacter rodentiumSialic acidMucusMucinColonizationBacteroidesBacteriaBiochemistryEcologyGene

Abstract

fetched live from OpenAlex

Abstract Background Our gut microbiota plays an important role in protecting the gastrointestinal (GI) tract from invading enteric pathogens. Much of this colonization resistance is mediated by limiting nutrient availability, however, enteric pathogens have evolved strategies to subvert this competition, utilizing commensal metabolites to facilitate their infection. Access to nutrients is not only crucial for a pathogen’s metabolic fitness, but can also drive the expression of virulence factors, a process high in energy demands. In addition, enteric pathogens, such as the attaching and effacing (A/E) bacterium Citrobacter rodentium, must cross the colonic mucus layer that normally prevents their direct access to the underlying epithelium. Intestinal mucus is comprised of highly glycosylated mucins, with the sugar sialic acid frequently occupying the terminal position of their O-glycan side chains. We hypothesize that C. rodentium utilizes commensal-liberated mucin sugars, such as sialic acid, as nutrients and signals to promote its virulence. Purpose This study investigates the mechanisms by which A/E pathogens reach the colonic mucosal surface, and the role played by commensal microbes in facilitating the infection. Method Expression of virulence factors secreted by C. rodentium in the presence or absence of sialic acid was analyzed by SDS-PAGE and mass spectrometry. Next, we infected specific-pathogen free (SPF), germfree (GF), and previously GF C57Bl/6 mice mono-colonized with Bacteroides thetaotaomicron, a mucus-degrading commensal, to examine their susceptibility to C. rodentium and to measure the levels of free sialic acid in their feces. Result(s) Sensing of sialic acid by C. rodentium, was found to induce the secretion of several key virulence proteins, enhancing the pathogen’s migration across the colonic mucus layer and adhesion to the underlying epithelium. Access to sialic acid within the gut environment was enhanced in the presence of microbiota, as the levels of free sialic acid were low in GF mice. Interestingly, despite GF mice carrying very high C. rodentium burdens, passage across the mucus layer and infection of their colonic epithelium was impaired as compared to SPF mice. Notably, B. thetaotaomicron was found to degrade whole mucus in vitro, facilitating its consumption by C. rodentium for growth, while B. thetaotaomicron mono-colonized GF mice showed increased susceptibility to colonic infection by C. rodentium. Conclusion(s) We demonstrate that although commensal microbes promote colonization resistance, as an A/E pathogen infection establishes, specific commensal bacteria accelerate infection in the GI tract by releasing an important nutrient, ie. sialic acid, from mucus. Access to sialic acid promotes C. rodentium virulence by inducing the key virulence factors that facilitate its translocation across the mucus layer as well as adhesion to the epithelium, thereby expediting disease progression. Please acknowledge all funding agencies by checking the applicable boxes below CCC, CIHR, Other Please indicate your source of funding; CH.I.L.D. Foundation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.010
GPT teacher head0.201
Teacher spread0.190 · 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 routes2
Has abstractyes

Explore more

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