Proteomic and machine learning signatures of rabies virus infection reveal stage-specific biomarkers

Rabies virus (RABV) is a highly neurotropic pathogen with near-uniform lethality after symptom onset, yet the molecular mechanisms underlying disease progression in the central nervous system (CNS) remain poorly defined. To address this gap, we performed time-resolved, label-free quantitative proteomics in RABV-infected mouse brains at defined clinical phases of RABV infection (asymptomatic, progressive, terminal). Differential protein expression was analyzed by clustering, enrichment, and protein-protein interaction networks. Machine learning models classified infection stages, and top biomarkers were validated by Western blot. Principal component and clustering analyses separated infection phases robustly, while GO/KEGG and PPI analyses revealed a progression from cytoskeletal/trafficking remodeling (early) to innate immune activation (intermediate) and proteostasis collapse/neurodegeneration-linked pathways (late). A Support Vector Machines classifier discriminated phases with high performance (F1 = 0.88; AUC = 0.79) and SHAP interpretation highlighted LAMP2, IL18 and SNCA among the top phase-specific predictors, and confirmed experimentally. This integrative proteomics-machine learning approach maps dynamic molecular transitions during RABV infection and nominates diagnostic biomarkers relevant to neurovirology. These findings provide mechanistic insights into viral neuropathogenesis and highlight parallels with neurodegeneration.