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Record W4213389452 · doi:10.1093/jcag/gwab049.198

A199 UTILIZATION OF MUCUS-DERIVED O-GLYCAN SUGARS BY THE PATHOGEN <i>C. RODENTIUM</i> FACILITATE ITS COLONIZATION OF THE MURINE GUT

2022· article· en· W4213389452 on OpenAlexaff
M Mslati, Hongbing Yu, Chenhui Ma, Bruce A. Vallance

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsCitrobacter rodentiumMicrobiologyBiologyMucinPathogenMucusSialic acidVirulenceGlycanHuman pathogenBacteriaBiochemistryGlycoproteinEcologyGene

Abstract

fetched live from OpenAlex

Abstract Background The ability of enteric pathogens to colonize and expand within the mammalian gastrointestinal (GI) tract is determined by several factors, including the ability to find and acquire nutrients. The thick mucus layer that lines the inner surface of the large intestine is rich in sugars that can serve as nutrient sources for several members of the microbiota. Whether these sugars can also be used by invading bacterial pathogens to colonize the GI tract is still unclear, in particular for the family of attaching and effacing (A/E) bacterial pathogens, including the human diarrheal pathogens EHEC and EPEC. Aims To investigate the ability of the murine A/E pathogen Citrobacter rodentium to use mucin-derived sugars as a nutrient source, and the importance of these sugars in the virulence of C. rodentium during in-vivo infection. Methods To identify which sugar(s) are required for C. rodentium to colonize and grow in the murine GI tract, we generated mutants lacking single or multiple genes involved in the uptake and catabolism of mucin-derived O-glycan sugars. This was followed by in-vitro growth assays in minimal media supplemented with mucin sugars to investigate the growth properties of C. rodentium and the generated mutants on mucin sugars. Results We determined that C. rodentium was able to use three mucin O-glycan sugars: sialic acid, galactose, and N-acetylglucosamine (GlcNAc) as both carbon and nitrogen sources for in-vitro growth. C. rodentium exhibited the maximal growth rate and density on GlcNAc, followed by sialic acid, and finally galactose. A mutant C. rodentium strain carrying a deletion in the nagA gene was unable to grow on both GlcNAc and sialic acid, confirming that the breakdown pathways for these two sugars merge and are processed by shared suite of enzymes. As for galactose, combined deletions in the genes mglB and galP were required to abolish growth on this sugar. Notably, a mutant strain carrying simultaneous deletions in nagA, mglB, and galP was unable to grow on all three mucin sugars, as well as on purified mucin. Conclusions Our results demonstrate that intestinal mucin sugars serve as potential nutrient sources for C. rodentium and that C. rodentium can catabolize three of these sugars. Future work will examine whether these sugar pathways contribute to C. rodentium colonization of the murine GI tract. Funding Agencies CCC, CIHRCH.I.L.D. Fdn

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.016
GPT teacher head0.192
Teacher spread0.177 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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