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Comparative Analysis of Human Tissue Interactomes Reveals Factors Leading to Tissue-Specific Manifestation of Hereditary Diseases

Overview of attention for article published in PLoS Computational Biology, June 2014
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Title
Comparative Analysis of Human Tissue Interactomes Reveals Factors Leading to Tissue-Specific Manifestation of Hereditary Diseases
Published in
PLoS Computational Biology, June 2014
DOI 10.1371/journal.pcbi.1003632
Pubmed ID
Authors

Ruth Barshir, Omer Shwartz, Ilan Y. Smoly, Esti Yeger-Lotem

Abstract

An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations that are present in cells across the human body. Here we analyzed this phenomenon for over 300 hereditary diseases by using comparative network analysis. We created an extensive resource of protein expression and interactions in 16 main human tissues, by integrating recent data of gene and protein expression across tissues with data of protein-protein interactions (PPIs). The resulting tissue interaction networks (interactomes) shared a large fraction of their proteins and PPIs, and only a small fraction of them were tissue-specific. Applying this resource to hereditary diseases, we first show that most of the disease-causing genes are widely expressed across tissues, yet, enigmatically, cause disease phenotypes in few tissues only. Upon testing for factors that could lead to tissue-specific vulnerability, we find that disease-causing genes tend to have elevated transcript levels and increased number of tissue-specific PPIs in their disease tissues compared to unaffected tissues. We demonstrate through several examples that these tissue-specific PPIs can highlight disease mechanisms, and thus, owing to their small number, provide a powerful filter for interrogating disease etiologies. As two thirds of the hereditary diseases are associated with these factors, comparative tissue analysis offers a meaningful and efficient framework for enhancing the understanding of the molecular basis of hereditary diseases.

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Geographical breakdown

Country Count As %
United States 4 3%
United Kingdom 2 2%
Switzerland 1 <1%
Sweden 1 <1%
Denmark 1 <1%
Poland 1 <1%
Unknown 116 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 24%
Researcher 26 21%
Student > Master 15 12%
Student > Bachelor 11 9%
Student > Postgraduate 9 7%
Other 17 13%
Unknown 18 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 28%
Agricultural and Biological Sciences 32 25%
Computer Science 10 8%
Medicine and Dentistry 8 6%
Engineering 4 3%
Other 15 12%
Unknown 22 17%