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

Overview of attention for article published in Molecular Autism, September 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

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16 tweeters
2 Wikipedia pages


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52 Mendeley
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Clustering the autisms using glutamate synapse protein interaction networks from cortical and hippocampal tissue of seven mouse models
Published in
Molecular Autism, September 2018
DOI 10.1186/s13229-018-0229-1
Pubmed ID

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


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. 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. 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. 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 19%
Student > Ph. D. Student 9 17%
Student > Master 7 13%
Student > Doctoral Student 5 10%
Student > Bachelor 4 8%
Other 6 12%
Unknown 11 21%
Readers by discipline Count As %
Neuroscience 11 21%
Biochemistry, Genetics and Molecular Biology 7 13%
Medicine and Dentistry 4 8%
Psychology 4 8%
Computer Science 3 6%
Other 10 19%
Unknown 13 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 November 2021.
All research outputs
of 19,496,717 outputs
Outputs from Molecular Autism
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Outputs of similar age
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Outputs of similar age from Molecular Autism
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Altmetric has tracked 19,496,717 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 617 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 29.0. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 242,805 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them