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Record W2791345509 · doi:10.1093/jcag/gwy009.274

A274 GUT MICROBIOTA REGULATES INNATE EPITHELIAL RESPONSES AGAINST AN ENTERIC BACTERIAL PATHOGEN

2018· article· en· W2791345509 on OpenAlexaff
Joannie M. Allaire, Hong Yang, Navjit Moore, Shauna M. Crowley, Elena F. Verdú, Bruce A. Vallance

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsMcMaster UniversityBC Children's Hospital
Fundersnot available
KeywordsInnate immune systemBiologyPathogenMicrobiologyDysbiosisImmune systemImmunologyGut floraColonisation resistanceAntibiotics

Abstract

fetched live from OpenAlex

Our gut microbiota plays a protective role in the GI tract, by promoting colonization resistance against enteric pathogens through nutrient competition. Antibiotic treatments in early life can lead to prolonged microbial dysbiosis and increase susceptibility to future enteric infections. Moreover, the gut microbiota have been shown to impact the development of the mucosal and systemic immune systems. Intestinal epithelial cells (IEC) are crucial cells that control interactions between the mucosal immune system and the luminal microbiota, but they also initiate innate immune responses to pathogens. Recent studies have explored the role played by the microbiota in controlling the virulence strategies of the attaching and effacing (A/E) microbe, Citrobacter rodentium (Cr). This pathogen directly adheres to IEC, causing diarrheal disease as well as inflammation, with commensal microbes impacting its ability to infect IEC, as well as being critical for its clearance. At present however, it is not clear whether commensal microbes influence the innate immune response by IEC to Cr infection. Defining the impact of commensal microbes on innate intestinal defenses may prove useful in developing new therapeutic approaches to combat enteric infections. To investigate the impact of commensal microbiota on innate IEC responses to Cr infection. This study compared the innate responses of specific-pathogen free (SPF) and germfree (GF) C57BL/6 mice to Cr infection. Pathogen burdens, pathology scores, qPCR and immunofluorescence staining were used to characterize the mucosal response. SPF and GF mice were orally infected and pathogen burdens determined on day 6 post-infection (6 DPI). Increased pathogen burdens were observed in GF mice as compared to SPF mice. Cecal tissues of GF mice showed increased pathology scores with evidence of increased Cr adherence to the mucosal surface. In contrast, GF mice showed reduced colon pathology scores than SPF mice, with reduced pathogen adherence. Apoptosis was characterized, with the ceca of GF mice at 6 DPI displaying massive IEC death and sloughing, while this was not seen in infected SPF mice. Interestingly, while SPF mice showed the expected upregulation in inflammatory (IL-22, IL-17A, IL-6) and antimicrobial (Reg3γ and Relmβ) responses in both the cecum and colon, GF mice showed a differential response, with dramatically higher levels of IL-22, but reduced expression of Reg3γ and Relmβ as compared to SPF mice. These findings indicate that commensal microbes play a key role in controlling epithelial responses to A/E pathogens, promoting some responses while suppressing others. Further studies will unravel the mechanisms underlying microbiota-based regulation of IEC responses to infection, with the goal of developing novel approaches to prevent human infection. CAG, CIHRFRQS, MSFHR

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 score0.004
Threshold uncertainty score0.012

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)0.0040.001

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 head0.010
GPT teacher head0.202
Teacher spread0.193 · 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
Published2018
Admission routes1
Has abstractyes

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