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Record W2921075952 · doi:10.1093/jcag/gwz006.155

A156 SIALIC ACID UTILIZATION IS ESSENTIAL FOR THE IN VIVO METABOLIC FITNESS OF THE ENTERIC BACTERIAL PATHOGEN CITROBACTER REDENTIUM

2019· article· en· W2921075952 on OpenAlexaff
Qiuli Liang, H Yu, Bruce A. Vallance

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsCitrobacter rodentiumMicrobiologySialic acidBiologyPathogenVirulenceIn vivoBiochemistryGeneGenetics

Abstract

fetched live from OpenAlex

The intestinal pathogens enteropathogenic and enterohemorrhagic E. coli (EPEC/EHEC) pose a significant threat to human health worldwide. These pathogens colonize host intestinal mucosal surface, forming attaching/effacing (A/E) lesions, and causing severe diarrheal disease. Currently it is unclear how resident commensals and nutritional sources in the gut influence these A/E pathogens during the course of infection. However, based on their location at the mucosal surface, they must closely interact with the intestinal mucus, which is largely composed of the highly glycosylated protein Muc2. As the most common terminal sugar on Muc2 glycans, sialic acid is actively cleaved by commensal microbes expressing sialidases, providing nutrition for themselves, as well as nearby bacteria. Recent studies suggest that host-derived sialic acid is important for the fitness of many intestinal pathogens. Citrobacter rodentium – a mouse A/E pathogen, contains genes necessary for transport and catabolism of sialic acid, however whether sialic acid plays a role in in vivo metabolism of A/E pathogens and their virulence remains unclear. To examine the role of sialic acid utilization in C. rodentium pathogenesis. A sialic acid transporter mutant of C. rodentium (ΔnanT) was constructed and tested in in vitro growth assays and in vivo infection, in comparison with wildtype (WT) C. rodentium. C57Bl/6 mice were infected with either WT or ΔnanT C. rodentium by oral gavage and monitored daily until euthanized at day 8 post infection (PI). In some infections, mice were pretreated with 20 mg streptomycin, 24 h before infection. In vitro growth assays showed that although WT C. rodentium was able to utilize sialic acid as a sole carbon source for growth, ΔnanT was unable to do so. Moreover, ΔnanT was significantly impaired in its ability to colonize the intestines of mice, being found at very low numbers in tissues and colonic lumen (102–103 CFU/g) at day 8 PI. In contrast, WT C. rodentium underwent rapid expansion in the colonic environment. Correspondingly, minimal histopathological damage was observed in ΔnanT infected mice. Interestingly, when mice were depleted of commensals by streptomycin, ΔnanT is able to readily colonize the intestines at comparable levels with the WT strain in terms of pathogen burdens, localization and pathology scores, suggesting the streptomycin freed up additional nutrients for ΔnanT. These results demonstrate that C. rodentium employs the nan transporter system for sialic acid utilization, which is essential for establishing its colonization in the mouse intestine. Moreover, these findings suggest that commensal microbes play a key role in controlling the availability of sialic acid in the colonic environment, thereby affecting the virulence and metabolism of A/E bacterial pathogens. CAG, CCC, CIHR

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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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
Published2019
Admission routes1
Has abstractyes

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