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Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed

MPG-Autoren
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Rieckmann,  Jan C.
Meissner, Felix / Experimental Systems Immunology, Max Planck Institute of Biochemistry, Max Planck Society;

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Meissner,  Felix
Meissner, Felix / Experimental Systems Immunology, Max Planck Institute of Biochemistry, Max Planck Society;

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Zitation

Kveler, K., Starosvetsky, E., Ziv-Kenet, A., Kalugny, Y., Gorelik, Y., Shalev-Malul, G., et al. (2018). Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed. Nature Biotechnology, 36(7), 651-659. doi:10.1038/nbt.4152.


Zitierlink: https://hdl.handle.net/21.11116/0000-0002-0B2B-3
Zusammenfassung
Cytokines are signaling molecules secreted and sensed by immune and other cell types, enabling dynamic intercellular communication. Although a vast amount of data on these interactions exists, this information is not compiled, integrated or easily searchable. Here we report immuneXpresso, a text-mining engine that structures and standardizes knowledge of immune intercellular communication. We applied immuneXpresso to PubMed to identify relationships between 340 cell types and 140 cytokines across thousands of diseases. The method is able to distinguish between incoming and outgoing interactions, and it includes the effect of the interaction and the cellular function involved. These factors are assigned a confidence score and linked to the disease. By leveraging the breadth of this network, we predicted and experimentally verified previously unappreciated cell-cytokine interactions. We also built a global immune-centric view of diseases and used it to predict cytokine-disease associations. This standardized knowledgebase (http://www.immunexpresso.org) opens up new directions for interpretation of immune data and model-driven systems immunology.