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Record W3040599042 · doi:10.1016/j.celrep.2020.107838

Atrx Deletion in Neurons Leads to Sexually Dimorphic Dysregulation of miR-137 and Spatial Learning and Memory Deficits

2020· article· en· W3040599042 on OpenAlexafffund
Renee J. Tamming, Vanessa Dumeaux, Yan Jiang, Sarfraz Shafiq, Luana Langlois, Jacob Ellegood, Lily R. Qiu, Jason P. Lerch, Nathalie G. Bérubé

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsUniversity of TorontoConcordia UniversityWestern UniversityLawson Health Research InstituteHospital for Sick ChildrenChildren’s Health Research Institute
FundersCanadian Institutes of Health ResearchWestern UniversityChildren's Health Research InstituteCanada First Research Excellence FundChild Health Foundation
KeywordsATRXBiologyNeuroscienceTelomereCell biologyGeneGeneticsMutation

Abstract

fetched live from OpenAlex

ATRX gene mutations have been identified in syndromic and non-syndromic intellectual disabilities in humans. ATRX is known to maintain genomic stability in neuroprogenitor cells, but its function in differentiated neurons and memory processes remains largely unresolved. Here, we show that the deletion of neuronal Atrx in mice leads to distinct hippocampal structural defects, fewer presynaptic vesicles, and an enlarged postsynaptic area at CA1 apical dendrite-axon junctions. We identify male-specific impairments in long-term contextual memory and in synaptic gene expression, linked to altered miR-137 levels. We show that ATRX directly binds to the miR-137 locus and that the enrichment of the suppressive histone mark H3K27me3 is significantly reduced upon the loss of ATRX. We conclude that the ablation of ATRX in excitatory forebrain neurons leads to sexually dimorphic effects on miR-137 expression and on spatial memory, identifying a potential therapeutic target for neurological defects caused by ATRX dysfunction.

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 score0.472
Threshold uncertainty score0.396

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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

Citations31
Published2020
Admission routes2
Has abstractyes

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