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Record W2790825542 · doi:10.7554/elife.30457

Neurexin–Neuroligin 1 regulates synaptic morphology and functions via the WAVE regulatory complex in Drosophila neuromuscular junction

2018· article· en· W2790825542 on OpenAlexafffund
Guanglin Xing, Moyi Li, Yichen Sun, Menglong Rui, Yan Zhuang, Huihui Lv, Junhai Han, Zhengping Jia, Wei Xie

Bibliographic record

VenueeLife · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsNeurexinPostsynaptic potentialNeuroliginNeuromuscular junctionCell biologyPostsynaptic densityBiologySynaptic vesicleSynaptic cleftNeurotransmissionNeuroscienceReceptorBiochemistryMembraneVesicle

Abstract

fetched live from OpenAlex

Neuroligins are postsynaptic adhesion molecules that are essential for postsynaptic specialization and synaptic function. But the underlying molecular mechanisms of neuroligin functions remain unclear. We found that Drosophila Neuroligin 1 (DNlg1) regulates synaptic structure and function through WAVE regulatory complex (WRC)-mediated postsynaptic actin reorganization. The disruption of DNlg1, DNlg2, or their presynaptic partner neurexin (DNrx) led to a dramatic decrease in the amount of F-actin. Further study showed that DNlg1, but not DNlg2 or DNlg3, directly interacts with the WRC via its C-terminal interacting receptor sequence. That interaction is required to recruit WRC to the postsynaptic membrane to promote F-actin assembly. Furthermore, the interaction between DNlg1 and the WRC is essential for DNlg1 to rescue the morphological and electrophysiological defects in dnlg1 mutants. Our results reveal a novel mechanism by which the DNrx-DNlg1 trans-synaptic interaction coordinates structural and functional properties at the neuromuscular junction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.762

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.276
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 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

Citations50
Published2018
Admission routes2
Has abstractyes

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