From mice to humans: A multi-omic predictive framework for translational immunology

Abstract

Mice are key preclinical animal models in vaccine and immunological research, yet their predictive value for human immunity remains contested. Here, we evaluated the translatability of murine models across inactivated and subunit vaccination (influenza, hepatitis B), acute infection (S. aureus, E. coli), and injury (burns and trauma), integrating transcriptomic profiles with sequence evolution, cis-regulatory architecture, and functional annotation. Functional modules were more conserved between species than individual orthologous genes. Translational accuracy depended on stimulus intensity, as infections and injuries engaged conserved signatures, while single-dose vaccination diverged. We then built multilayer models to predict human expression rank and direction of change, and to classify shared leading-edge genes. Adding evolutionary and regulatory layers improved these predictions. We provide a step-by-step R Markdown notebook to apply the models to user data. The study code and datasets are available at https://github.com/wapsyed/mousetohuman_multilayer