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Related Concept Videos

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Related Experiment Video

Updated: Jun 11, 2026

A Robust Polymerase Chain Reaction-based Assay for Quantifying Cytosine-guanine-guanine Trinucleotide Repeats in Fragile X Mental Retardation-1 Gene
08:22

A Robust Polymerase Chain Reaction-based Assay for Quantifying Cytosine-guanine-guanine Trinucleotide Repeats in Fragile X Mental Retardation-1 Gene

Published on: September 16, 2019

MCVAE-based multi-omic anomaly detection in Fragile X Syndrome.

Wassila Khatir1,2, Marco Lorenzi1, Irene Balelli1

  • 1INRIA Center at Université Côte d'Azur, Epione Team, 2004 Rte des Lucioles, 06560 Valbonne, France.

NAR Molecular Medicine
|June 10, 2026
PubMed
Summary

A new AI model, Multi-Channel Variational Autoencoder (MCVAE), identifies coordinated molecular changes in Fragile X Syndrome (FXS) by analyzing gene expression and protein synthesis. This approach reveals subtle disruptions in the neurodevelopmental disorder brain.

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Last Updated: Jun 11, 2026

A Robust Polymerase Chain Reaction-based Assay for Quantifying Cytosine-guanine-guanine Trinucleotide Repeats in Fragile X Mental Retardation-1 Gene
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11:10

Dissecting Cell-Autonomous Function of Fragile X Mental Retardation Protein in an Auditory Circuit by In Ovo Electroporation

Published on: July 6, 2022

Area of Science:

  • Neuroscience
  • Computational Biology
  • Genetics

Background:

  • Fragile X Syndrome (FXS) is a genetic neurodevelopmental disorder caused by mutations in the FMR1 gene, leading to the absence of Fragile X Messenger Ribonucleoprotein (FMRP).
  • FMRP regulates the translation of numerous mRNAs, and its loss disrupts the coordination between transcriptomic and translatomic data in the FXS brain.
  • Existing omics analyses face challenges due to limited sample availability and dataset heterogeneity, hindering the detection of subtle, coordinated multi-omic dysregulations.

Purpose of the Study:

  • To develop a computational framework for uncovering coordinated molecular perturbations in FXS.
  • To leverage a Multi-Channel Variational Autoencoder (MCVAE) to integrate transcriptomic and translatomic data for improved analysis.
  • To identify potential biomarkers and therapeutic targets for FXS by understanding its underlying pathophysiology.

Main Methods:

  • Trained a Multi-Channel Variational Autoencoder (MCVAE) on wild-type samples to learn a shared latent representation of transcriptomic and translatomic data through cross-modal reconstruction.
  • Applied the trained MCVAE to Fmr1 knock-out samples (modeling FXS) to detect deviations from wild-type patterns as anomalies.
  • Utilized publicly available databases to explore coordinated relationships between transcriptomic and translatomic anomalies and their mapping to known biological pathways.

Main Results:

  • The MCVAE successfully identified known and novel molecular perturbations in the Fmr1 knock-out model.
  • MCVAE demonstrated superior performance compared to alternative methods, showing stronger enrichment for FMRP mRNA targets and enhanced genotype discriminative power.
  • Detected coordinated anomalies between transcriptomic and translatomic layers that mapped to validated FMRP regulators and crucial neurodevelopmental pathways.

Conclusions:

  • MCVAE serves as a powerful framework for uncovering coordinated molecular dysregulations in complex diseases like FXS.
  • The identified anomalies provide insights into the pathophysiology of FXS, highlighting the interplay between gene expression and protein translation.
  • This approach facilitates the identification of novel biomarkers and therapeutic targets for Fragile X Syndrome.