Database Commons

a catalog of worldwide biological databases

Tissue contexts are extremely valuable when studying protein functions and their associated phenotypes. Recently, the study of proteins in tissue contexts was greatly facilitated by the availability of thousands of tissue transcriptomes. To provide access to these data we developed the TissueNet integrative database that displays protein-protein interactions (PPIs) in tissue contexts. Through TissueNet, users can create tissue-sensitive network views of the PPI landscape of query proteins. Unlike other tools, TissueNet output networks highlight tissue-specific and broadly expressed proteins, as well as over- and under-expressed proteins per tissue. The TissueNet v.3 upgrade has a much larger dataset of proteins and PPIs, and represents 125 adult tissues and seven embryonic tissues. Thus, TissueNet provides an extensive, quantitative, and user-friendly interface to study the roles of human proteins in adulthood and embryonic stages. TissueNet v3 is freely available at https://netbio.bgu.ac.il/tissuenet3.

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.