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Record W3027905648 · doi:10.7554/elife.55342

MeCP2 in cholinergic interneurons of nucleus accumbens regulates fear learning

2020· article· en· W3027905648 on OpenAlexaff
Ying Zhang, Yi Zhu, Shu-Xia Cao, Peng Sun, Jian-Ming Yang, Yanfang Xia, Shi‐Ze Xie, Xiaodan Yu, Jiayu Fu, C. Shen, Haiyang He, Haoqi Pan, Xiaojuan Chen, Hao Wang, Xiao‐Ming Li

Bibliographic record

VenueeLife · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsUniversity of Toronto
FundersChinese Academy of Medical SciencesGovernment of Guangdong ProvinceGuangdong Science and Technology DepartmentMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMECP2Rett syndromeNeuroscienceNucleus accumbensCholinergicBiologyInhibitory postsynaptic potentialNeurotransmissionGABAergicReceptorGeneGeneticsCentral nervous systemPhenotype

Abstract

fetched live from OpenAlex

Methyl-CpG-binding protein 2 (MeCP2) encoded by the MECP2 gene is a transcriptional regulator whose mutations cause Rett syndrome (RTT). Mecp2-deficient mice show fear regulation impairment; however, the cellular and molecular mechanisms underlying this abnormal behavior are largely uncharacterized. Here, we showed that Mecp2 gene deficiency in cholinergic interneurons of the nucleus accumbens (NAc) dramatically impaired fear learning. We further found that spontaneous activity of cholinergic interneurons in Mecp2-deficient mice decreased, mediated by enhanced inhibitory transmission via α2-containing GABAA receptors. With MeCP2 restoration, opto- and chemo-genetic activation, and RNA interference in ChAT-expressing interneurons of the NAc, impaired fear retrieval was rescued. Taken together, these results reveal a previously unknown role of MeCP2 in NAc cholinergic interneurons in fear regulation, suggesting that modulation of neurons in the NAc may ameliorate fear-related disorders.

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 score0.174
Threshold uncertainty score0.390

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 head0.013
GPT teacher head0.237
Teacher spread0.223 · 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 routes1
Has abstractyes

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