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Record W2795560655 · doi:10.1073/pnas.1720956115

Identification of long-lived synaptic proteins by proteomic analysis of synaptosome protein turnover

2018· article· en· W2795560655 on OpenAlexfundno aff
Seok Heo, Graham H. Diering, Chan Hyun Na, Raja Sekhar Nirujogi, Julia L. Bachman, Akhilesh Pandey, Richard L. Huganir

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsnot available
FundersGovernment of CanadaNational Institutes of HealthNational Cancer InstituteNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthCanadian Institutes of Health Research
KeywordsSynaptosomeSynaptic plasticityProtein turnoverCytosolCell biologyBiologyProteomicsNeuroplasticityNeuroscienceChemistryProtein biosynthesisBiochemistryCentral nervous systemEnzyme

Abstract

fetched live from OpenAlex

Memory formation is believed to result from changes in synapse strength and structure. While memories may persist for the lifetime of an organism, the proteins and lipids that make up synapses undergo constant turnover with lifetimes from minutes to days. The molecular basis for memory maintenance may rely on a subset of long-lived proteins (LLPs). While it is known that LLPs exist, whether such proteins are present at synapses is unknown. We performed an unbiased screen using metabolic pulse-chase labeling in vivo in mice and in vitro in cultured neurons combined with quantitative proteomics. We identified synaptic LLPs with half-lives of several months or longer. Proteins in synaptic fractions generally exhibited longer lifetimes than proteins in cytosolic fractions. Protein turnover was sensitive to pharmacological manipulations of activity in neuronal cultures or in mice exposed to an enriched environment. We show that synapses contain LLPs that may underlie stabile long-lasting changes in synaptic structure and function.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.022
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.005
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.359
Teacher spread0.298 · 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

Citations173
Published2018
Admission routes1
Has abstractyes

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