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Record W2143720746 · doi:10.4049/jimmunol.0903170

SHIP Is Required for Dendritic Cell Maturation

2010· article· en· W2143720746 on OpenAlexafffund
Frann Antignano, Mariko Ibaraki, Connie Kim, Jens Rüschmann, Angela Zhang, Cheryl D. Helgason, Gerald Krystal

Bibliographic record

VenueThe Journal of Immunology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsBC Cancer AgencyTerry Fox Research Institute
FundersBC Cancer AgencyTerry Fox FoundationMichael Smith Health Research BC
KeywordsCell biologyWortmanninRegulatorStimulationDendritic cellIn vivoBiologyPI3K/AKT/mTOR pathwayImmune systemSignal transductionImmunologyEndocrinologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Although several groups have investigated the role of SHIP in macrophage (M) development and function, SHIP's contribution to the generation, maturation, and innate immune activation of dendritic cells (DCs) is poorly understood. We show herein that SHIP negatively regulates the generation of DCs from bone marrow precursors in vitro and in vivo, as illustrated by the enhanced expansion of DCs from SHIP(-/-) GM-CSF cultures, as well as increased numbers of DCs in the spleens of SHIP-deficient mice. Interestingly, however, these SHIP(-/-) DCs display a relatively immature phenotype and secrete substantially lower levels of IL-12 after TLR ligand stimulation than wild type DCs. This, in turn, leads to a dramatically reduced stimulation of Ag-specific T cell proliferation and Th1 cell responses in vitro and in vivo. This immature phenotype of SHIP(-/-) DCs could be reversed with the PI3K inhibitors LY294002 and wortmannin, suggesting that SHIP promotes DC maturation by reducing the levels of the PI3K second messenger phosphatidylinositol-3,4,5-trisphosphate. These results are consistent with SHIP being a negative regulator of GM-CSF-derived DC generation but a positive regulator of GM-CSF-derived DC maturation and function.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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 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

Citations40
Published2010
Admission routes2
Has abstractyes

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