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Involvement of Fps/Fes and Fer Kinases in c-Kit Signalling in Mast Cells and Mast Cell Leukemias.

2005· article· en· W2586305743 on OpenAlexaff
Lionel A. Samayawardhena, Andrew W. Craig

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsQueen's University
Fundersnot available
KeywordsStem cell factorMast cellKinaseCancer researchTyrosine kinaseCell biologyProto-oncogene tyrosine-protein kinase SrcReceptor tyrosine kinasePhosphorylationBiologySignal transductionChemistryMolecular biologyHaematopoiesisImmunologyStem cell

Abstract

fetched live from OpenAlex

Abstract The c-Kit receptor protein-tyrosine kinase (PTK) is required for mast cell differentiation and function. Signalling by c-Kit normally depends on binding its ligand, Stem Cell Factor (SCF). However, several constitutively activating mutations in c-Kit have been described in myeloid leukemias (including mast cell leukemias (MCLs)) and in solid tumors. The objective of this study is to define the involvement of two non-receptor PTKs called Fps/Fes (hereafter referred to as Fps) and Fer in c-Kit signaling in mast cells and MCLs. Recent studies using transgenic mouse strains bearing kinase-inactivating or null mutations in Fer and Fps have revealed roles in hematopoiesis, and in limiting the innate immune response. Recently, we showed that Fer and Fps are activated downstream of the high affinity IgE receptor, FcεRI, in bone marrow-derived mast cells (BMMCs), and that Fer kinase is required for sustained p38 Mitogen-activated protein kinase (MAPK) activation and chemotaxis upon activation of either FcεRI or c-Kit. In this study, we focus on the roles of Fer and Fps in c-Kit signaling in BMMCs. SCF-induced c-Kit activation led to rapid association of both kinases with c-Kit, and increased tyrosine phosphorylation of Fer and Fps. Interestingly, kinase-dead Fps protein was also phosphorylated upon c-Kit activation, suggesting the involvement of an upstream kinase. Pretreatment of BMMCs with the Src family kinase inhibitor SU6656, caused reduced phosphorylation of Fer and Fps upon SCF treatment. This suggests an involvement of one or more Src family kinases (SFKs) in phosphorylating Fps and Fer upon c-Kit activation. We next examined the potential involvement of the Fyn SFK, since it is known to interact with a juxtamembrane site in activated c-Kit and has been linked to Fer activation in other cell types. SCF treatment of BMMCs from Fyn knock-out mice (fyn-null) revealed normal activation of c-Kit, Erk MAPK, but reduced phosphorylation of Fer kinase. Furthermore, we have observed reduced phosphorylation of Shp2 phosphatase, and the p38 and Jnk MAPKs in Fyn-null BMMCs compared to control. We are currently attempting to restore Fyn expression by retroviral transduction to attempt to rescue these signalling defects, and also evaluate potential requirements for Fyn in chemotaxis and cytokine production. Using MCL cell lines harbouring constitutively actived c-Kit (RBL-2H3 and HMC-1), we have observed that phosphorylation of Fer and Fps kinases are inhibited by treating cells with either a c-Kit inhibitor (SU11652) or SFK inhibitor (SU6656). In conclusion, Fer and Fps kinases participate in c-Kit signaling in normal and leukemic mast cells, and Src family kinases likely contribute to their activation. Using mast cells from Fer/Fps-deficient mice, we will identify their downstream targets and determine if they are required for leukemogenesis by an activated c-Kit transgene.

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 score0.003

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 integrity0.0000.000
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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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
Published2005
Admission routes1
Has abstractyes

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