MétaCan
Menu
Back to cohort
Record W2998554651 · doi:10.1016/j.celrep.2019.11.087

Control of Long-Term Synaptic Potentiation and Learning by Alternative Splicing of the NMDA Receptor Subunit GluN1

2019· article· en· W2998554651 on OpenAlexafffund
Ameet S. Sengar, Hongbin Li, Wenbo Zhang, Celeste Leung, Arun Ramani, Ner Mu Nar Saw, Yongqian Wang, YuShan Tu, P. Joel Ross, Stephen W. Scherer, James Ellis, Michael Brudno, Zhengping Jia, Michael W. Salter

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsSickKids FoundationUniversity of Prince Edward IslandUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchGénome QuébecOntario Brain InstituteGenome CanadaSimons Foundation
KeywordsLong-term potentiationNeuroscienceSynaptic plasticityNMDA receptorHippocampusBiologyNeurotransmissionAlternative splicingMetaplasticityNeuroplasticityExonReceptorGeneticsGene

Abstract

fetched live from OpenAlex

NMDA receptors (NMDARs) are critical for physiological synaptic plasticity, learning, and memory and for pathological plasticity and neuronal death. The GluN1 subunit is encoded by a single gene, GRIN1, with 8 splice variants, but whether the diversity generated by this splicing has physiological consequences remains enigmatic. Here, we generate mice lacking from the GluN1 exon 5-encoded N1 cassette (GluN1a mice) or compulsorily expressing this exon (GluN1b mice). Despite no differences in basal synaptic transmission, long-term potentiation in the hippocampus is significantly enhanced in GluN1a mice compared with that in GluN1b mice. Furthermore, GluN1a mice learn more quickly and have significantly better spatial memory performance than do GluN1b mice. In addition, in human iPSC-derived neurons in autism spectrum disorder NMDARs show characteristics of N1-lacking GluN1. Our findings indicate that alternative splicing of GluN1 is a mechanism for controlling physiological long-lasting synaptic potentiation, learning, and memory.

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 score0.107
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.275
Teacher spread0.260 · 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

Citations63
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCell ReportsSame topicNeuroscience and Neuropharmacology ResearchFrench-language works237,207