Predictive multi-omic biomarkers for urban zoonotic spillover detection: an integrative review

Urban wildlife is an overlooked yet critical component of zoonotic disease surveillance, especially in biodiversity hotspots where human-animal interfaces accelerate spillover risk. This review synthesizes five complementary omics layers: Host microRNAs, host-pathogen genetic markers, bacterial microbiome profiling, viromics, and host transcriptomics into a single predictive framework for early spillover detection. Across taxa and pathogen classes, we highlight convergent molecular signatures of infection, from receptor polymorphisms and shifts in MHC diversity to pathogen-responsive miRNAs, high-risk bacterial genera, novel viral sequences, and transcriptomic profiles associated with pathogen tolerance. By integrating these biomarkers into a cross-validated, multi-omics architecture, we outline a workflow from non-invasive sampling to predictive modeling that enhances sensitivity for detecting both known and cryptic pathogens. We also identify key barriers, including Field preservation, cross-species assay standardization, and bioinformatics capacity, and propose practical solutions, such as interoperable pipelines and open-access databases. This integrative approach shifts surveillance from reactive detection to anticipatory risk profiling, providing a transformative tool for One Health strategies aimed at forecasting and preventing zoonotic epidemics.