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Postsynaptic Clustering and Activation of Pyk2 by PSD-95

2010· article· en· W1963811715 on OpenAlexafffund
Jason A. Bartos, Jason D. Ulrich, Hongbin Li, Michael A. Beazely, Yucui Chen, John F. MacDonald, Johannes Hell

Bibliographic record

VenueJournal of Neuroscience · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsWestern UniversityUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCanadian Institutes of Health ResearchUniversity of North Carolina at Chapel HillNational Institutes of HealthNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institute of Mental HealthAmerican Heart Association
KeywordsPostsynaptic potentialNeuroscienceCluster analysisPostsynaptic densityPsychologyChemistryExcitatory postsynaptic potentialComputer scienceArtificial intelligenceInhibitory postsynaptic potentialBiochemistryReceptor

Abstract

fetched live from OpenAlex

The tyrosine kinase Pyk2 plays a unique role in intracellular signal transduction by linking Ca(2+) influx to tyrosine phosphorylation, but the molecular mechanism of Pyk2 activation is unknown. We report that Pyk2 oligomerization by antibodies in vitro or overexpression of PSD-95 in PC6-3 cells induces trans-autophosphorylation of Tyr402, the first step in Pyk2 activation. In neurons, Ca(2+) influx through NMDA-type glutamate receptors causes postsynaptic clustering and autophosphorylation of endogenous Pyk2 via Ca(2+)- and calmodulin-stimulated binding to PSD-95. Accordingly, Ca(2+) influx promotes oligomerization and thereby autoactivation of Pyk2 by stimulating its interaction with PSD-95. We show that this mechanism of Pyk2 activation is critical for long-term potentiation in the hippocampus CA1 region, which is thought to underlie learning and memory.

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 integrity0.0000.001
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 head0.049
GPT teacher head0.353
Teacher spread0.304 · 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

Citations93
Published2010
Admission routes2
Has abstractyes

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