Concordance of gene expression in human protein complexes reveals tissue specificity and pathology

Daniela Börnigen,Curtis Huttenhower,Yves Moreau,Tune H Pers,Lieven Thorrez,Søren Brunak

doi:10.1093/nar/gkt661

Daniela Börnigen, Curtis Huttenhower + Show 4 more

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https://doi.org/10.1093/nar/gkt661

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Abstract

Disease-causing variants in human genes usually lead to phenotypes specific to only a few tissues. Here, we present a method for predicting tissue specificity based on quantitative deregulation of protein complexes. The underlying assumption is that the degree of coordinated expression among proteins in a complex within a given tissue may pinpoint tissues that will be affected by a mutation in the complex and coordinated expression may reveal the complex to be active in the tissue. We identified known disease genes and their protein complex partners in a high-quality human interactome. Each susceptibility gene's tissue involvement was ranked based on coordinated expression with its interaction partners in a non-disease global map of human tissue-specific expression. The approach demonstrated high overall area under the curve (0.78) and was very successfully benchmarked against a random model and an approach not using protein complexes. This was illustrated by correct tissue predictions for three case studies on leptin, insulin-like-growth-factor 2 and the inhibitor of NF-κB kinase subunit gamma that show high concordant expression in biologically relevant tissues. Our method identifies novel gene-phenotype associations in human diseases and predicts the tissues where associated phenotypic effects may arise.

Highlights

Most genes and their protein products function as modules within biological networks, their tissue-specific functions in metazoans have largely been analysed from the perspective of individual disease genes
We introduce TissueRanker, a predictive method for disease/tissue associations based on a co-expression measure of transcripts within human protein complexes, leveraging a recently published global map of human gene expression data [7]
We used 3590 protein complexes and a recently published global map of human gene expression data in TissueRanker to predict the tissue specificity of disease phenotypes arising from defects in these complexes

Summary

Introduction

Most genes and their protein products function as modules within biological networks, their tissue-specific functions in metazoans have largely been analysed from the perspective of individual disease genes. For example, quantified the expression of >6000 proteins in individual tissues [1], and these data typically target individual proteins, they can be linked with gene expression data to begin to analyse the disease-specific functions of entire complexes. A strategy was developed for systematically correlating the manifestations of diseases with expression patterns of genes and protein complexes across human tissues [1]. This established a significant tendency for disease genes to be over-expressed in tissues where defects cause pathology, even under non-disease conditions. More than 1000 diseases e171 Nucleic Acids Research, 2013, Vol 41, No 18

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