Contributions of F-BAR and SH2 Domains of Fes Protein Tyrosine Kinase for Coupling to the FcεRI Pathway in Mast Cells
Bibliographic record
Abstract
This study investigates the roles of Fer-CIP4 homology (FCH)-Bin/amphiphysin/Rvs (F-BAR) and SH2 domains of Fes protein tyrosine kinase in regulating its activation and signaling downstream of the high-affinity immunoglobulin G (IgE) receptor (FcepsilonRI) in mast cells. Homology modeling of the Fes F-BAR domain revealed conservation of some basic residues implicated in phosphoinositide binding (R113/K114). The Fes F-BAR can bind phosphoinositides and induce tubulation of liposomes in vitro. Mutation of R113/K114 to uncharged residues (RK/QQ) caused a significant reduction in phosphoinositide binding in vitro and a more diffuse cytoplasmic localization in transfected COS-7 cells. RBL-2H3 mast cells expressing full-length Fes carrying the RK/QQ mutation show defects in FcepsilonRI-induced Fes tyrosine phosphorylation and degranulation compared to cells expressing wild-type Fes. This correlated with reduced localization to Lyn kinase-containing membrane fractions for the RK/QQ mutant compared to wild-type Fes in mast cells. The Fes SH2 domain also contributes to Fes signaling in mast cells, via interactions with the phosphorylated FcepsilonRI beta chain and the actin regulatory protein HS1. We show that Fes phosphorylates C-terminal tyrosine residues in HS1 implicated in actin stabilization. Thus, coordinated actions of the F-BAR and SH2 domains of Fes allow for coupling to FcepsilonRI signaling and potential regulation the actin reorganization in mast cells.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".