Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. Autism spectrum disorder (ASD) is a complex genetic trait that encompasses a range of neurodevelopmental disorders. Here, the authors clone brain-expressed alternatively-spliced isoforms of ASD risk factors and construct a network of protein interactions that provides further insight into the disease aetiology.
Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism
R. Corominas,Xinping Yang,G. Lin,Shuli Kang,Yun Shen,L. Ghamsari,M. Broly,Maria D. Rodriguez,Stanley Tam,Shelly A. Wanamaker,Changyu Fan,S. Yi,M. Tasan,I. Lemmens,Xingyan Kuang,Nan Zhao,D. Malhotra,J. Michaelson,V. Vacic,M. Calderwood,F. Roth,J. Tavernier,S. Horvath,K. Salehi-Ashtiani,Dmitry Korkin,J. Sebat,D. Hill,Tong Hao,M. Vidal,L. Iakoucheva
Published 2014 in Nature Communications
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- Publication year
2014
- Venue
Nature Communications
- Publication date
2014-04-11
- Fields of study
Biology, Medicine
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Semantic Scholar, PubMed
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