Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Alex J. Cornish, Ioannis Filippis, Alessia David, Michael J.E. Sternberg

ABSTRACT

BACKGROUND: Each cell type found within the human body performs a diverse and unique set of functions, the disruption of which can lead to disease. However, there currently exists no systematic mapping between cell types and the diseases they can cause. METHODS: In this study, we integrate protein-protein interaction data with high-quality cell-type-specific gene expression data from the FANTOM5 project to build the largest collection of cell-type-specific interactomes created to date. We develop a novel method, called gene set compactness (GSC), that contrasts the relative positions of disease-associated genes across 73 cell-type-specific interactomes to map genes associated with 196 diseases to the cell types they affect. We conduct text-mining of the PubMed database to produce an independent resource of disease-associated cell types, which we use to validate our method. RESULTS: The GSC method successfully identifies known disease-cell-type associations, as well as highlighting associations that warrant further study. This includes mast cells and multiple sclerosis, a cell population currently being targeted in a multiple sclerosis phase 2 clinical trial. Furthermore, we build a cell-type-based diseasome using the cell types identified as manifesting each disease, offering insight into diseases linked through etiology. CONCLUSIONS: The data set produced in this study represents the first large-scale mapping of diseases to the cell types in which they are manifested and will therefore be useful in the study of disease systems. Overall, we demonstrate that our approach links disease-associated genes to the phenotypes they produce, a key goal within systems medicine. More... »

PAGES

95

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13073-015-0212-9

    DOI

    http://dx.doi.org/10.1186/s13073-015-0212-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1018432604

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26330083


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