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Record W4211083722 · doi:10.1101/2022.02.10.480004

PP1 inhibitor-2 promotes PP1γ positive regulation of synaptic transmission

2022· preprint· en· W4211083722 on OpenAlexafffund
Karl Foley, Haider F. Altimimi, Hailong Hou, Yu Zhang, Cody McKee, Makaía M. Papasergi-Scott, Hongtian Yang, Abigail Mayer, Nancy E. Ward, David M. MacLean, Angus C. Nairn, David Stellwagen, Houhui Xia

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of CanadaNational Institutes of HealthNational Science Foundation
KeywordsProtein phosphatase 1NeurotransmissionSynaptic plasticityFörster resonance energy transferPhosphorylationCell biologyPhosphataseHippocampal formationNeuroscienceChemistryBiophysicsBiologyBiochemistryFluorescenceReceptor

Abstract

fetched live from OpenAlex

Abstract Inhibitor-2 (I-2) is a prototypic inhibitor of protein phosphatase-1 (PP1), a major serine-threonine phosphatase that regulates synaptic plasticity and learning and memory. Although I-2 is a potent inhibitor of PP1 in vitro, our previous work has elucidated that, in vivo, I-2 may act as a positive regulator of PP1. Here we show that I-2 and PP1γ, but not PP1α, positively regulate synaptic transmission in hippocampal neurons. Moreover, we demonstrated that I-2 enhances PP1γ interaction with its major synaptic scaffold, neurabin, by Förster resonance energy transfer (FRET)/Fluorescence lifetime imaging microscopy (FLIM) studies, while having a limited effect on PP1 autoinhibitory phosphorylation. Furthermore, our study indicates that the effect of I-2 on PP1 activity in vivo is dictated by I-2 threonine-72 phosphorylation. Our work thus demonstrates a molecular mechanism by which I-2 positively regulates PP1 function in synaptic transmission.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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 teacher head, not a consensus.

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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

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