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The Relationship Between Glutamate Dynamics and Activity-Dependent Synaptic Plasticity

2020· article· en· W3007147784 on OpenAlexaff
Jocelyn R. Barnes, Bandhan Mukherjee, Ben Rogers, Firoozeh Nafar, Madeline Gosse, Matthew P. Parsons

Bibliographic record

VenueJournal of Neuroscience · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMemorial University of Newfoundland
FundersStrong
KeywordsGlutamate receptorNeuroscienceSynaptic plasticityLong-term potentiationNeurotransmissionAMPA receptorBiologyMetabotropic glutamate receptorLTP inductionChemistryExcitatory postsynaptic potentialInhibitory postsynaptic potentialBiochemistryReceptor

Abstract

fetched live from OpenAlex

The spatiotemporal dynamics of excitatory neurotransmission must be tightly regulated to achieve efficient synaptic communication. By limiting spillover, glutamate transporters are believed to prevent excessive activation of extrasynaptically located receptors that can impair synaptic plasticity. While glutamate transporter expression is reduced in numerous neurodegenerative diseases, the contributions of transporter dysfunction to disease pathophysiology remain ambiguous as the fundamental relationship between glutamate dynamics and plasticity, and the mechanisms linking these two phenomena, remain poorly understood. Here, we combined electrophysiology and real-time high-speed imaging of extracellular glutamate transients during LTP induction and characterized the sensitivity of the relationship between glutamate dynamics during theta burst stimulation (TBS) and the resulting magnitude of LTP consolidation, both in control conditions and following selective and nonselective glutamate transporter blockade. Glutamate clearance times were negatively correlated with LTP magnitude following nonselective glutamate transporter inhibition but not following selective blockade of a majority of GLT-1, the brain's most abundant glutamate transporter. Although glutamate transporter inhibition reduced the postsynaptic population response to TBS, calcium responses to TBS were greatly exaggerated. The source of excess calcium was dependent on NMDARs, L-type VGCCs, GluA2-lacking AMPARs, and internal calcium stores. Surprisingly, inhibition of L-type VGCCs, but not GluA2-lacking AMPARs or ryanodine receptors, was required to restore robust LTP. In all, these data provide a detailed understanding of the relationship between glutamate dynamics and plasticity and uncover important mechanisms by which poor glutamate uptake can negatively impact LTP consolidation. SIGNIFICANCE STATEMENTSpecific patterns of neural activity can promote long-term changes in the strength of synaptic connections through a phenomenon known as synaptic plasticity. Synaptic plasticity is well accepted to represent the cellular mechanisms underlying learning and memory, and many forms of plasticity are initiated by the excitatory neurotransmitter glutamate. While essential for rapid cellular communication in the brain, excessive levels of extracellular glutamate can negatively impact brain function. In this study, we demonstrate that pharmacological manipulations that increase the availability of extracellular glutamate during neural activity can have profoundly negative consequences on synaptic plasticity. We identify mechanisms through which excess glutamate can negatively influence synaptic plasticity, and we discuss the relevance of these findings to neurodegenerative diseases and in the aging brain.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.135
GPT teacher head0.366
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations93
Published2020
Admission routes1
Has abstractyes

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