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Published in: Molecular Autism 1/2018

Open Access 01-12-2018 | Research

Clustering the autisms using glutamate synapse protein interaction networks from cortical and hippocampal tissue of seven mouse models

Authors: Emily A. Brown, Jonathan D. Lautz, Tessa R. Davis, Edward P. Gniffke, Alison A. W. VanSchoiack, Steven C. Neier, Noah Tashbook, Chiara Nicolini, Margaret Fahnestock, Adam G. Schrum, Stephen E. P. Smith

Published in: Molecular Autism | Issue 1/2018

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Abstract

Background

Autism spectrum disorders (ASDs) are a heterogeneous group of behaviorally defined disorders and are associated with hundreds of rare genetic mutations and several environmental risk factors. Mouse models of specific risk factors have been successful in identifying molecular mechanisms associated with a given factor. However, comparisons among different models to elucidate underlying common pathways or to define clusters of biologically relevant disease subtypes have been complicated by different methodological approaches or different brain regions examined by the labs that developed each model. Here, we use a novel proteomic technique, quantitative multiplex co-immunoprecipitation or QMI, to make a series of identical measurements of a synaptic protein interaction network in seven different animal models. We aim to identify molecular disruptions that are common to multiple models.

Methods

QMI was performed on 92 hippocampal and cortical samples taken from seven mouse models of ASD: Shank3B, Shank3Δex4-9, Ube3a2xTG, TSC2, FMR1, and CNTNAP2 mutants, as well as E12.5 VPA (maternal valproic acid injection on day 12.5 post-conception). The QMI panel targeted a network of 16 interacting, ASD-linked, synaptic proteins, probing 240 potential co-associations. A custom non-parametric statistical test was used to call significant differences between ASD models and littermate controls, and Hierarchical Clustering by Principal Components was used to cluster the models using mean log2 fold change values.

Results

Each model displayed a unique set of disrupted interactions, but some interactions were disrupted in multiple models. These tended to be interactions that are known to change with synaptic activity. Clustering revealed potential relationships among models and suggested deficits in AKT signaling in Ube3a2xTG mice, which were confirmed by phospho-western blots.

Conclusions

These data highlight the great heterogeneity among models, but suggest that high-dimensional measures of a synaptic protein network may allow differentiation of subtypes of ASD with shared molecular pathology.
Appendix
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Metadata
Title
Clustering the autisms using glutamate synapse protein interaction networks from cortical and hippocampal tissue of seven mouse models
Authors
Emily A. Brown
Jonathan D. Lautz
Tessa R. Davis
Edward P. Gniffke
Alison A. W. VanSchoiack
Steven C. Neier
Noah Tashbook
Chiara Nicolini
Margaret Fahnestock
Adam G. Schrum
Stephen E. P. Smith
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Molecular Autism / Issue 1/2018
Electronic ISSN: 2040-2392
DOI
https://doi.org/10.1186/s13229-018-0229-1

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