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Record W3088660704 · doi:10.1101/2020.09.22.301762

Pioglitazone improves deficits of <i>Fmr1</i> -KO mouse model of Fragile X syndrome by interfering with excessive diacylglycerol signaling

2020· preprint· en· W3088660704 on OpenAlexaff
Andréa Geoffroy, Karima Habbas, Boglárka Zámbó, Laetitia Schramm, Arnaud Duchon, Eric Flatter, Laëtitia Fouillen, Julie Zumsteg, Dimitri Heintz, Jean‐Louis Mandel, Yann Hérault, Hervé Moine

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsUniversity of Ottawa
FundersCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheFRAXA Research Foundation
KeywordsFragile X syndromeFMR1Diacylglycerol kinaseNeuroscienceSignal transductionBiologyCell biologyEndocrinologyInternal medicineMedicineFragile xProtein kinase CGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Fragile X syndrome (FXS), the leading cause of familial intellectual disability, is an uncured disease caused by the absence or loss of function of the FMRP protein. FMRP is an RNA binding protein that controls the translation of specific proteins in neurons. A main target of FMRP in neurons is diacylglycerol kinase kappa (DGKk) and the loss of FMRP leads to a loss of DGK activity causing a diacylglycerol excess in the brain. Excessive diacylglycerol signaling could be a significant contributor to the pathomechanism of FXS. Here we tested the contribution of DAG-signaling in Fmr1 -KO mouse model of FXS and we show that pioglitazone, a widely prescribed drug for type 2 diabetes, has ability to correct excessive DAG signaling in the brain and rescue behavioral alterations of the Fmr1 -KO mouse. This study highlights the role of lipid signaling homeostasis in FXS and provides arguments to support the testing of pioglitazone for treatment of FXS.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.190
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2020
Admission routes1
Has abstractyes

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