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Record W4320733310 · doi:10.1186/s40478-023-01524-x

Asymmetric dysregulation of glutamate dynamics across the synaptic cleft in a mouse model of Alzheimer’s disease

2023· article· en· W4320733310 on OpenAlexafffund
Kyle J. Brymer, Emily P. Hurley, Jessica C. Barron, Bandhan Mukherjee, Jocelyn R. Barnes, Firoozeh Nafar, Matthew P. Parsons

Bibliographic record

VenueActa Neuropathologica Communications · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaAlzheimer SocietyAlzheimer's SocietySuncor Energy Incorporated
KeywordsNeuroscienceNeurologyGlutamate receptorDiseaseMedicinePsychologyBiologyPathologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

Most research on glutamate spillover focuses on the deleterious consequences of postsynaptic glutamate receptor overactivation. However, two decades ago, it was noted that the glial coverage of hippocampal synapses is asymmetric: astrocytic coverage of postsynaptic sites exceeds coverage of presynaptic sites by a factor of four. The fundamental relevance of this glial asymmetry remains poorly understood. Here, we used the glutamate biosensor iGluSnFR, and restricted its expression to either CA3 or CA1 neurons to visualize glutamate dynamics at pre- and postsynaptic microenvironments, respectively. We demonstrate that inhibition of the primarily astrocytic glutamate transporter-1 (GLT-1) slows glutamate clearance to a greater extent at presynaptic compared to postsynaptic membranes. GLT-1 expression was reduced early in a mouse model of AD, resulting in slower glutamate clearance rates at presynaptic but not postsynaptic membranes that opposed presynaptic short-term plasticity. Overall, our data demonstrate that the presynapse is particularly vulnerable to GLT-1 dysfunction and may have implications for presynaptic impairments in a variety of brain diseases.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0000.001
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 head0.146
GPT teacher head0.388
Teacher spread0.242 · 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

Citations18
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueActa Neuropathologica CommunicationsSame topicNeuroscience and Neuropharmacology ResearchFrench-language works237,207