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Record W4323350804 · doi:10.1093/jcag/gwac036.178

A178 ELUCIDATING THE ROLE OF THE LEUCINE-RICH REPEAT KINASE 2 G2019S MUTATION IN CROHN’S DISEASE PATHOGENESIS USING A CITROBACTER RODENTIUM INFECTIOUS COLITIS MODEL

2023· article· en· W4323350804 on OpenAlexaff
Bana Samman, Jitender Yadav, Giuliano Bayer, Elisabeth G. Foerster, L Chen, Juliana Dutra Barbosa da Rocha, Stephen E. Girardin, Dana J. Philpott

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsCitrobacter rodentiumImmunologyBiologyColitisPathogenesisPathogenCitrobacterLRRK2InflammationInflammatory bowel diseaseImmune systemSpleenMicrobiologyDiseaseMedicinePathologyEnterobacterParkinson's diseaseGeneEscherichia coliGenetics

Abstract

fetched live from OpenAlex

Abstract Background Associations have been found linking certain LRRK2 kinase domain gain-of-function variants, such as G2019S, to the development of Crohn’s disease and Parkinson’s disease, yet their exact roles in pathogenesis remains elusive. LRRK2 is most robustly expressed in circulating and tissue-resident immune cells, such as neutrophils, lymphocytes, and macrophages. Myeloid cells deficient in LRRK2 exhibit defective antimicrobial responses, such as reduced production of reactive oxygen species in response to microbial stimuli and reduced bactericidal activity in response to infection. As an enteric colitis-inducing extracellular pathogen, Citrobacter rodentium can help us better understand the consequences of LRRK2 kinase hyperactivity on intestinal inflammation by correlating pathogen burden with key host response parameters over the course of infection. Purpose To investigate the effects of the Crohn’s and Parkinson’s disease-associated Lrrk2 G2019S hyper-kinase mutation on pathogen burden and colonic inflammation in the context of C. rodentium-induced infectious colitis. Method Wild-type and Lrrk2 G2019S mutant mice (7-8 weeks old) were fasted for 4 hours then infected with 1 x 108 CFU of C. rodentium in a 3% NaHCO3 solution by oral gavage. Body weight, faecal pathogen burden, and faecal lipocalin-2 (Lcn2) concentrations were measured at 2, 4, 7, 9, 10, 12, and 14 days post-infection (DPI). Systemic pathogen burden (as measured in the mesenteric lymph nodes and spleen), colon length, colonic inflammatory gene expression, and histopathological scoring were assessed at 7, 10, and 14 DPI. Result(s) While G2019S mice exhibited marginally higher C. rodentium loads at certain timepoints, no significant differences were found in overall pathogen burden or pathogen clearance rates between genotypes over the first 14 days of infection. Faecal pathogen load peaked at 7-9 DPI in both WT and G2019S mice, which correlated with detectable levels of C. rodentium in the mesenteric lymph nodes and spleens of some mice at 7 DPI. Lcn2 secretion and the expression of inflammatory and antimicrobial genes of interest were induced robustly over the course of infection. They peaked and ebbed at timepoints correlating well with pathogen burden; however, no significant differences were observed between WT and G2019S mutant mice at the various timepoints assessed. Conclusion(s) Mice expressing the G2019S Lrrk2 mutation exhibit neither defective pathogen control nor deleterious hyperinflammation – compared to WT mice – when infected with C. rodentium. Further research will aim to investigate the role of this Lrrk2 variant in additional models of intestinal inflammation. Please acknowledge all funding agencies by checking the applicable boxes below CCC, CIHR 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 score0.004

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 integrity0.0010.001
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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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 routes1
Has abstractyes

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