Abstract
BackgroundInter-tissue molecular interactions are critical to the function and behavior of biological systems in multicellular organisms, but systematic studies of interactions between tissues are lacking. Also, existing studies of inter-tissue interactions are based on direct gene expression correlations, which can’t distinguish correlations due to common genetic architectures versus biochemical or molecular signal exchange between tissues.MethodsWe developed a novel strategy to study inter-tissue interaction by removing effects of genetic regulation of gene expression (genetic decorrelation). We applied our method to the comprehensive atlas of gene expression across nine human tissues in the Genotype-Tissue Expression (GTEx) project to generate novel genetically decorrelated inter-tissue networks. From this we derived modules of genes important in inter-tissue interactions that are likely driven by biological signal exchange instead of their common genetic basis. Importantly we highlighted communication between tissues and elucidated gene activities in one tissue inducing gene expression changes in others.ResultsWe reveal global unidirectional inter-tissue coordination of specific biological pathways such as protein synthesis. Using our data, we highlighted a clinically relevant example whereby heart expression of DPP4 was coordinated with a gene expression signature characteristic for whole blood proliferation, potentially impacting peripheral stem cell mobilization. We also showed that expression of the poorly characterized FOCAD in heart correlated with protein biosynthetic processes in the lung.ConclusionsIn summary, this is the first resource of human multi-tissue networks enabling the investigation of molecular inter-tissue interactions. With the networks in hand, we may systematically design combination therapies that simultaneously target multiple tissues or pinpoint potential side effects of a drug in other tissues.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0268-1) contains supplementary material, which is available to authorized users.
Highlights
Inter-tissue molecular interactions are critical to the function and behavior of biological systems in multicellular organisms, but systematic studies of interactions between tissues are lacking
We developed a systematic method to quantify the reasonable numbers of probabilistic estimation of expression residuals (PEER) factors to be adjusted in each tissue with respect to the optimal gene ontology (GO) enrichment (Notes and Figure S1 in Additional file 2)
The major goal and novelty of this study is to explore biological interactions between tissues, in the absence of the potentially confounding common genetic contributions to different genes [4] due to common regulatory elements or shared expression quantitative trait loci, which were reported in the Genotype-Tissue Expression (GTEx) paper [5]
Summary
Inter-tissue molecular interactions are critical to the function and behavior of biological systems in multicellular organisms, but systematic studies of interactions between tissues are lacking. Existing studies of inter-tissue interactions are based on direct gene expression correlations, which can’t distinguish correlations due to common genetic architectures versus biochemical or molecular signal exchange between tissues. Tissues in multicellular organisms do not operate in isolation, but interact with other tissues and organ systems. Abundant large-scale data on protein–protein interactions and gene–gene interactions [1,2,3] in single tissues have been reported, large scale unbiased interactions across tissues are currently less well characterized. Therapeutically targeting a gene in one tissue may cause side effects or beneficial effects in distant tissues. A systematic method of uncovering tissue–tissue interactions in an unbiased way is urgently needed
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