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.

Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. We developed a machine learning model that leverages human and mouse public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and show it is able to identify 20-50% more human-relevant differentially expressed genes. FIT predicted novel disease-associated genes, an example of which we validated experimentally in Crohn's patients. FIT highlights signals that may otherwise be missed and reduces false leads with no experimental cost. It is available both as an R package and as a web tool.