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Impact of genetic variation on synaptic protein levels in genetically diverse mice
Author(s) -
Loos Maarten,
Li Ka Wan,
der Schors Roel,
Gouwenberg Yvonne,
der Loo Rolinka,
Williams Robert W.,
Smit August B.,
Spijker Sabine
Publication year - 2016
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201500154
Subject(s) - proteome , biology , transcriptome , genetics , genetic variation , synapse , computational biology , evolutionary biology , gene , gene expression , neuroscience
The relative abundance of synaptic proteins shapes protein complex formation and is essential for synapse function and behavioral fitness. Here, we have used a panel of highly diverse inbred strains of mice—NOD/LtJ, A/J, 129S1/SvImJ, FVB/NJ, C57BL/6J, WSB/EiJ, PWK/PhJ, and CAST/EiJ—to quantify the effects of genetic variation on the synaptic proteome between strains. Using iTRAQ‐based quantitative proteome analyses, we detected significant differences in ∼20% of 400 core synaptic proteins. Surprisingly, the differentially abundant proteins showed a modest range of variation across strains, averaging about 1.3‐fold. Analysis of protein abundance covariation across the eight strains identified known protein–protein relations (proteins of Arp2/3 complex), as well as novel relations (e.g. Dlg family, Fscn1). Moreover, covariation of synaptic proteins was substantially tighter (∼fourfold more dense than chance level) than corresponding networks of synaptic transcripts (∼twofold more dense than chance). The tight stoichiometry and coherent synaptic protein covariation networks suggest more intense evolutionary selection at this level of molecular organization. In conclusion, genetic diversity in the mouse genome differentially affects the transcriptome and proteome, and only partially penetrates the synaptic proteome. Protein abundance correlation analyses in genetically divergent strains can complement protein–protein interaction network analyses, to provide insight into protein interactomes.