Found In Translation: a machine learning model for mouse-to-human inference.

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. Here we present Found In Translation (FIT; http://www.mouse2man.org ), a statistical methodology that leverages 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 identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20-50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost.

Social recognition in laboratory mice requires integration of behaviorally-induced somatosensory, auditory and olfactory cues.

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 mice do not exhibit recognition of 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 behavioral 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 mice relies on integration of information from several sensory modalities.

B cell-derived cfDNA after primary BNT162b2 mRNA vaccination anticipates memory B cells and SARS-CoV-2 neutralizing antibodies.

BACKGROUND: Much remains unknown regarding the response of the immune system to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccination. METHODS: We employed circulating cell-free DNA (cfDNA) to assess the turnover of specific immune cell types following administration of the Pfizer/BioNTech vaccine. FINDINGS: The levels of B cell cfDNA after the primary dose correlated with development of neutralizing antibodies and memory B cells after the booster, revealing a link between early B cell turnover-potentially reflecting affinity maturation-and later development of effective humoral response. We also observed co-elevation of B cell, T cell, and monocyte cfDNA after the booster, underscoring the involvement of innate immune cell turnover in the development of humoral and cellular adaptive immunity. Actual cell counts remained largely stable following vaccination, other than a previously demonstrated temporary reduction in neutrophil and lymphocyte counts. CONCLUSIONS: Immune cfDNA dynamics reveal the crucial role of the primary SARS-CoV-2 vaccine in shaping responses of the immune system following the booster vaccine. FUNDING: This work was supported by a generous gift from Shlomo Kramer. Supported by grants from Human Islet Research Network (HIRN UC4DK116274 and UC4DK104216 to R.S. and Y.D.), Ernest and Bonnie Beutler Research Program of Excellence in Genomic Medicine, The Alex U Soyka Pancreatic Cancer Fund, The Israel Science Foundation, the Waldholtz/Pakula family, the Robert M. and Marilyn Sternberg Family Charitable Foundation, the Helmsley Charitable Trust, Grail, and the DON Foundation (to Y.D.). Y.D. holds the Walter and Greta Stiel Chair and Research Grant in Heart Studies. I.F.-F. received a fellowship from the Glassman Hebrew University Diabetes Center.

Distinct dynamics of social motivation drive differential social behavior in laboratory rat and mouse strains.

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.

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

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.