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Record W3015207228 · doi:10.1038/s41467-020-15590-5

P2X7 receptor inhibition ameliorates dendritic spine pathology and social behavioral deficits in Rett syndrome mice

2020· article· en· W3015207228 on OpenAlexfundno aff
Juan Mauricio Garré, Hernandez Moura Silva, Juan J. Lafaille, Guang Yang

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthConselho Nacional de Desenvolvimento Científico e TecnológicoYork UniversityU.S. Department of Health and Human Services
KeywordsRett syndromeDendritic spineMECP2NeuroscienceMedicineSPINE (molecular biology)PathologyBiologyBioinformaticsGeneticsPhenotype

Abstract

fetched live from OpenAlex

Dysregulated immunity has been implicated in the pathogenesis of neurodevelopmental disorders but its contribution to synaptic and behavioral deficits in Rett syndrome (RTT) remains unknown. P2X7 receptors (P2X7Rs) are unique purinergic receptors with pro-inflammatory functions. Here, we report in a MECP2-deficient mouse model of RTT that the border of the cerebral cortex exhibits increased number of inflammatory myeloid cells expressing cell-surface P2X7Rs. Total knockout of P2X7Rs in MECP2 deficient mice decreases the number of inflammatory myeloid cells, restores cortical dendritic spine dynamics, and improves the animals' neurological function and social behavior. Furthermore, either genetic depletion of P2X7Rs in bone-marrow derived leukocytes or pharmacological block of P2X7Rs primarily outside of the central nervous system parenchyma, recapitulates the beneficial effects of total P2X7R depletion on the social behavior. Together, our results highlight the pathophysiological roles of P2X7Rs in a mouse model of RTT.

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 score0.864
Threshold uncertainty score0.489

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 head0.020
GPT teacher head0.279
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

Citations42
Published2020
Admission routes1
Has abstractyes

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