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Record W2118283102 · doi:10.1128/iai.00759-10

Nod1 and Nod2 Regulation of Inflammation in the <i>Salmonella</i> Colitis Model

2010· article· en· W2118283102 on OpenAlexafffund
Kaoru Geddes, Stephen Rubino, Catherine Streutker, João G. Magalhães, Lionel Le Bourhis, Thirumahal Selvanantham, Stephen E. Girardin, Dana J. Philpott

Bibliographic record

VenueInfection and Immunity · 2010
Typearticle
Languageen
FieldMedicine
TopicAmoebic Infections and Treatments
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchCrohn's and Colitis Foundation of CanadaCrohn's and Colitis FoundationHoward Hughes Medical Institute
KeywordsNOD1NOD2ColitisInflammationSalmonellaBiologyImmunologyMicrobiologyImmune systemInnate immune systemBacteriaGenetics

Abstract

fetched live from OpenAlex

The pattern recognition molecules Nod1 and Nod2 play important roles in intestinal homeostasis; however, how these proteins impact on the development of inflammation during bacterial colitis has not been examined. In the streptomycin-treated mouse model of Salmonella colitis, we found that mice deficient for both Nod1 and Nod2 had attenuated inflammatory pathology, reduced levels of inflammatory cytokines, and increased colonization of the mucosal tissue. Nod1 and Nod2 from both hematopoietic and nonhematopoietic sources contributed to the pathology, and all phenotypes were recapitulated in mice deficient for the signaling adaptor protein Rip2. However, the influence of Rip2 was strictly dependent on infection conditions that favored expression of the Salmonella pathogenicity island 2 (SPI-2) type III secretion system (TTSS), as Rip2 was dispensable for inflammation when mice were infected with bacteria grown under conditions that promoted expression of the SPI-1 TTSS. Thus, Nod1 and Nod2 can modulate inflammation and mediate efficient clearance of bacteria from the mucosal tissue during Salmonella colitis, but their role is dependent on the expression of the SPI-2 TTSS.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations122
Published2010
Admission routes2
Has abstractyes

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