Abelson tyrosine kinase links PDGFbeta receptor activation to cytoskeletal regulation of NMDA receptors in CA1 hippocampal neurons
Bibliographic record
Abstract
BACKGROUND: We have previously demonstrated that PDGF receptor activation indirectly inhibits N-methyl-D-aspartate (NMDA) currents by modifying the cytoskeleton. PDGF receptor ligand is also neuroprotective in hippocampal slices and cultured neurons. PDGF receptors are tyrosine kinases that control a variety of signal transduction pathways including those mediated by PLCγ. In fibroblasts Src and another non-receptor tyrosine kinase, Abelson kinase (Abl), control PDGF receptor regulation of cytoskeletal dynamics. The mechanism whereby PDGF receptor regulates cytoskeletal dynamics in central neurons remains poorly understood. RESULTS: Intracellular applications of active Abl, but not heat-inactivated Abl, decreased NMDA-evoked currents in isolated hippocampal neurons. This mimics the effects of PDGF receptor activation in these neurons. The Abl kinase inhibitor, STI571, blocked the inhibition of NMDA currents by Abl. We demonstrate that PDGF receptors can activate Abl kinase in hippocampal neurons via mechanisms similar to those observed previously in fibroblasts. Furthermore, PDGFβ receptor activation alters the subcellular localization of Abl. Abl kinase is linked to actin cytoskeletal dynamics in many systems. We show that the inhibition of NMDA receptor currents by Abl kinase is blocked by the inclusion of the Rho kinase inhibitor, Y-27632, and that activation of Abl correlates with an increase in ROCK tyrosine phosphorylation. CONCLUSION: This study demonstrates that PDGFβ receptors act via an interaction with Abl kinase and Rho kinase to regulated cytoskeletal regulation of NMDA receptor channels in CA1 pyramidal neurons.
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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".