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.

Mice and rats are widely used to explore mechanisms of mammalian social behavior in health and disease, raising the question whether they actually differ in their social behavior. Here we address this question by directly comparing social investigation behavior between two mouse and rat strains used most frequently for behavioral studies and as models of neuropathological conditions: C57BL/6 J mice and Sprague Dawley (SD) rats. Employing novel experimental systems for behavioral analysis of both subjects and stimuli during the social preference test, we reveal marked differences in behavioral dynamics between the strains, suggesting stronger and faster induction of social motivation in SD rats. These different behavioral patterns, which correlate with distinctive c-Fos expression in social motivation-related brain areas, are modified by competition with non-social rewarding stimuli, in a strain-specific manner. Thus, these two strains differ in their social behavior, which should be taken into consideration when selecting an appropriate model organism.

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.

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In humans, discrimination between individuals, also termed social recognition, can rely on a single sensory modality, such as vision. By analogy, social recognition in rodents is thought to be based upon olfaction. Here, we hypothesized that social recognition in rodents relies upon integration of olfactory, auditory and somatosensory cues, hence requiring active behavior of social stimuli. Using distinct social recognition tests, we demonstrated that adult male rats and mice do not recognize familiar stimuli or learn the identity of novel stimuli that are inactive due to anesthesia. We further revealed that impairing the olfactory, somatosensory or auditory systems prevents recognition of familiar stimuli. Finally, we found that familiar and novel stimuli generate distinct movement patterns during social discrimination and that subjects react differentially to the movement of these stimuli. Thus, unlike what occurs in humans, social recognition in rats and mice relies on integration of information from several sensory modalities.