MétaCan
Menu
Back to cohort
Record W4289109726 · doi:10.3390/cells11152354

Chronic AMPK Activation Reduces the Expression and Alters Distribution of Synaptic Proteins in Neuronal SH-SY5Y Cells

2022· article· en· W4289109726 on OpenAlexaff
Alex Yang, M. Ayaz Ahmad, Evangelia Tsiani, Aleksandar Necakov, Rebecca E. K. MacPherson

Bibliographic record

VenueCells · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsBrock University
Fundersnot available
KeywordsAMPKAMP-activated protein kinaseProtein kinase AmTORC1SynapseCell biologyNeuriteScaffold proteinBiologyPhosphorylationChemistrySignal transductionNeuroscienceBiochemistryPI3K/AKT/mTOR pathway

Abstract

fetched live from OpenAlex

Neuronal growth and synaptic function are dependent on precise protein production and turnover at the synapse. AMPK-activated protein kinase (AMPK) represents a metabolic node involved in energy sensing and in regulating synaptic protein homeostasis. However, there is ambiguity surrounding the role of AMPK in regulating neuronal growth and health. This study examined the effect of chronic AMPK activation on markers of synaptic function and growth. Retinoic-acid-differentiated SH-SY5Y human neuroblastoma cells were treated with A-769662 (100 nM) or Compound C (30 nM) for 1, 3, or 5 days before AMPK, mTORC1, and markers for synapse function were examined. Cell morphology, neuronal marker content, and location were quantified after 5 days of treatment. AMPK phosphorylation was maintained throughout all 5 days of treatment with A-769662 and resulted in chronic mTORC1 inhibition. Lower total, soma, and neuritic neuronal marker contents were observed following 5 d of AMPK activation. Neurite protein abundance and distribution was lower following 5 days of A-769662 treatment. Our data suggest that chronic AMPK activation impacts synaptic protein content and reduces neurite protein abundance and distribution. These results highlight a distinct role that metabolism plays on markers of synapse health 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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.301
Teacher spread0.266 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCellsSame topicNeuroscience and Neuropharmacology ResearchFrench-language works237,207