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Accession IconGSE47150

Microarray profiling of primary neurons transduced with shRNA for multiple ASD-implicated genes

Organism Icon Mus musculus
Sample Icon 37 Downloadable Samples
Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

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Austism spectrum disorder (ASD) is a heterogeneous behavioral disease most commonly characterized by severe impairment of social engagement and the presence of repetitive activities. The molecular etiology of ASD is still largely unknown despite a strong genetic component. Part of the difficulty in turning genetics into disease mechanisms and potentially new therapeutics is the sheer number and diversity of the genes that have been associated with ASD and ASD symptoms. The goal of this work is to use shRNA-generated models of genetic defects proposed as causative for ASD to identify the common pathways that might explain how they produce a common clinical outcome. Transcript levels of Mecp2, Mef2a, Mef2d, Fmr1, Nlgn1, Nlgn3, Pten, and Shank3 were knocked-down in mouse primary neuron cultures using shRNA/lentivirus constructs. Whole genome expression analysis was conducted for each of the knock-down cultures as well as a mock-transduced culture and a culture exposed to a lentivirus expressing luciferase. Gene set enrichment and a causal reasoning engine were employed to indentify pathway level perturbations generated by the transcript knock-down. Quantitation of the shRNA targets confirmed the successful knock-down at the transcript and protein levels of at least 75% for each of the genes. After subtracting out potential artifacts caused by transfection and viral infection, gene set enrichment and causal reasoning engine analysis showed that a significant number of gene expression changes mapped to pathways associated with neurogenesis, long-term potentiation, and synaptic activity. This work demonstrates that despite the complex genetic nature of ASD, there are common molecular mechanisms that connect many of the best established autism candidate genes. By identifying the key regulatory checkpoints in the interlinking transcriptional networks underlying autism, we are better able to discover the ideal points of intervention that provide the broadest efficacy across the diverse population of autism patients.
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