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Record W3005528192 · doi:10.1128/mbio.03256-19

The HopQ-CEACAM Interaction Controls CagA Translocation, Phosphorylation, and Phagocytosis of <i>Helicobacter pylori</i> in Neutrophils

2020· article· en· W3005528192 on OpenAlexaff
Ina‐Kristin Behrens, Benjamin Busch, Hellen Ishikawa‐Ankerhold, Pia Palamides, John E. Shively, Cliff Stanners, Carlos H.F. Chan, Nelly Leung, Scott D. Gray‐Owen, Rainer Haas

Bibliographic record

VenuemBio · 2020
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsUniversity of TorontoMcGill University Health Centre
FundersDeutsche Forschungsgemeinschaft
KeywordsCagAHelicobacter pyloriPhagocytosisSecretionMicrobiologyChromosomal translocationPhagosomeBiologyPhosphorylationImmunologyCell biologyVirulenceGeneBiochemistry

Abstract

fetched live from OpenAlex

Helicobacter pylori is highly adapted to humans and evades host immunity to allow its lifelong colonization. However, the H. pylori mouse model is artificial for H. pylori , and few adapted strains allow gastric colonization. Here, we show that human or CEACAM-humanized, but not mouse neutrophils are manipulated by the H. pylori HopQ-CEACAM interaction. Human CEACAMs are responsible for CagA phosphorylation, activation, and processing in neutrophils, whereas CagA translocation and tyrosine phosphorylation in DCs and macrophages is independent of the HopQ-CEACAM interaction. H. pylori affects the secretion of distinct chemokines in CEACAM-humanized neutrophils and macrophages. Most importantly, human CEACAMs on neutrophils enhance binding, oxidative burst, and phagocytosis of H. pylori and enhance bacterial survival in the phagosome. The H. pylori -CEACAM interaction modulates PMNs to reduce the H. pylori CagA translocation efficiency in vivo and to fine-tune the expression of CEACAM receptors on neutrophils to limit translocation of CagA and gastric pathology.

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 score0.089
Threshold uncertainty score0.528

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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

Citations49
Published2020
Admission routes1
Has abstractyes

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