GSK3β Interacts With CRMP2 and Notch1 and Controls T-Cell Motility
Bibliographic record
Abstract
The trafficking of T-cells through peripheral tissues and into afferent lymphatic vessels is essential for immune surveillance and an adaptive immune response. Glycogen synthase kinase 3β (GSK3β) is a serine/threonine kinase and regulates numerous cell/tissue-specific functions, including cell survival, metabolism, and differentiation. Here, we report a crucial involvement of GSK3β in T-cell motility. Inhibition of GSK3β by CHIR-99021 or siRNA-mediated knockdown augmented the migratory behavior of human T-lymphocytes stimulated via an engagement of the T-cell integrin LFA-1 with its ligand ICAM-1. Proteomics and protein network analysis revealed ongoing interactions among GSK3β, the surface receptor Notch1 and the cytoskeletal regulator CRMP2. LFA-1 stimulation in T-cells reduced Notch1-dependent GSK3β activity by inducing phosphorylation at Ser9 and its nuclear translocation accompanied by the cleaved Notch1 intracellular domain and decreased GSK3β-CRMP2 association. LFA-1-induced or pharmacologic inhibition of GSK3β in T-cells diminished CRMP2 phosphorylation at Thr514. Although substantial amounts of CRMP2 were localized to the microtubule-organizing center in resting T-cells, this colocalization of CRMP2 was lost following LFA-1 stimulation. Moreover, the migratory advantage conferred by GSK3β inhibition in T-cells by CHIR-99021 was lost when CRMP2 expression was knocked-down by siRNA-induced gene silencing. We therefore conclude that GSK3β controls T-cell motility through interactions with CRMP2 and Notch1, which has important implications in adaptive immunity, T-cell mediated diseases and LFA-1-targeted therapies.
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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".