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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Iana Suly Santos Katz,
  • Elaine Raniero Fernandes,
  • Fernanda Guedes,
  • Sandriana Ramos Silva,
  • Alfonso Portabales Souza,
  • Koushik Vuttha,
  • Leo Kei Iwai

摘要

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.