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Record W4388014806 · doi:10.1242/jcs.261563

Cholesterol is required for activity-dependent synaptic growth

2023· article· en· W4388014806 on OpenAlexafffund
Amber Shaheen, Adam Sghaier, Jeffrey S. Dason

Bibliographic record

VenueJournal of Cell Science · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyAdenylyl cyclasePhosphodiesteraseSynaptic plasticityCholesterolProtein kinase ACell biologyEndocrinologyInternal medicinePhosphorylationBiochemistrySignal transductionEnzymeReceptor

Abstract

fetched live from OpenAlex

Changes in cholesterol content of neuronal membranes occur during development and brain aging. Little is known about whether synaptic activity regulates cholesterol levels in neuronal membranes and whether these changes affect neuronal development and function. We generated transgenic flies that express the cholesterol-binding D4H domain of perfringolysin O toxin and found increased levels of cholesterol in presynaptic terminals of Drosophila larval neuromuscular junctions following increased synaptic activity. Reduced cholesterol impaired synaptic growth and largely prevented activity-dependent synaptic growth. Presynaptic knockdown of adenylyl cyclase phenocopied the impaired synaptic growth caused by reducing cholesterol. Furthermore, the effects of knocking down adenylyl cyclase and reducing cholesterol were not additive, suggesting that they function in the same pathway. Increasing cAMP levels using a dunce mutant with reduced phosphodiesterase activity failed to rescue this impaired synaptic growth, suggesting that cholesterol functions downstream of cAMP. We used a protein kinase A (PKA) sensor to show that reducing cholesterol levels reduced presynaptic PKA activity. Collectively, our results demonstrate that enhanced synaptic activity increased cholesterol levels in presynaptic terminals and that these changes likely activate the cAMP-PKA pathway during activity-dependent growth.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.338
Teacher spread0.277 · 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.

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

Citations10
Published2023
Admission routes2
Has abstractyes

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