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Record W3046066211 · doi:10.1016/j.celrep.2020.107955

Postsynaptic and Presynaptic NMDARs Have Distinct Roles in Visual Circuit Development

2020· article· en· W3046066211 on OpenAlexafffund
Philip Kesner, Anne Schohl, Elodie C. Warren, Fan Ma, Edward S. Ruthazer

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMontreal Neurological Institute and HospitalMcGill University
FundersCanadian Institutes of Health Research
KeywordsGene knockdownPostsynaptic potentialAMPA receptorNeuroscienceNMDA receptorInhibitory postsynaptic potentialExcitatory postsynaptic potentialXenopusBiologyCell biologyReceptorChemistryBiochemistry

Abstract

fetched live from OpenAlex

To study contributions of N-methyl-D-aspartate receptors (NMDARs) in presynaptic and postsynaptic neurons of the developing visual system, we microinject antisense Morpholino oligonucleotide (MO) against GluN1 into one cell of two-cell-stage Xenopus laevis embryos. The resulting bilateral segregation of MO induces postsynaptic NMDAR (postNMDAR) knockdown in tectal neurons on one side and presynaptic NMDAR (preNMDAR) knockdown in ganglion cells projecting to the other side. PostNMDAR knockdown reduces evoked NMDAR- and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-mediated retinotectal currents. Although the frequency of spontaneous synaptic events is increased, the probability of evoked release is reduced. PreNMDAR knockdown results in larger evoked and unitary synaptic responses. Structurally, postNMDAR and preNMDAR knockdown produce complementary effects. Axonal arbor complexity is reduced by preNMDAR-MO and increased by postNMDAR-MO, whereas tectal dendritic arbors exhibit the inverse. The current study illustrates distinct roles for pre- and postNMDARs in circuit development and reveals extensive transsynaptic regulation of form 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)0.001
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 score0.010
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.053
GPT teacher head0.311
Teacher spread0.257 · 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

Citations21
Published2020
Admission routes2
Has abstractyes

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