Abstract
Borrelia burgdorferi, the agent of Lyme disease, is estimated to cause >400,000 annual infections in the United States. Serology is the primary laboratory method to support the diagnosis of Lyme disease, but current methods have intrinsic limitations that require alternative approaches or targets. We used a high-density peptide array that contains >90,000 short overlapping peptides to catalog immunoreactive linear epitopes from >60 primary antigens of B. burgdorferi. We then pursued a machine learning approach to identify immunoreactive peptide panels that provide optimal Lyme disease serodiagnosis and can differentiate antibody responses at various stages of disease. We examined 226 serum samples from the Lyme Biobank and the National Institutes of Health, which included sera from 110 individuals diagnosed with Lyme disease, 31 probable cases from symptomatic individuals, and 85 healthy controls. Cases were grouped based on disease stage and presentation and included individuals with early localized, early disseminated, and late Lyme disease. We identified a peptide panel originating from 14 different epitopes that differentiated cases versus controls, whereas another peptide panel built from 12 unique epitopes differentiated subjects with various disease manifestations. Our method demonstrated an improvement in B. burgdorferi antibody detection over the current two-tiered testing approach and confirmed the key diagnostic role of VlsE and FlaB antigens at all stages of Lyme disease. We also uncovered epitopes that triggered a temporal antibody response that was useful for differentiation of early and late disease. Our findings can be used to streamline serologic targets and improve antibody-based diagnosis of Lyme disease. IMPORTANCE: Serology is the primary method of Lyme disease diagnosis, but this approach has limitations, particularly early in disease. Currently employed antibody detection assays can be improved by the identification of alternative immunodominant epitopes and the selection of optimal diagnostic targets. We employed high-density peptide arrays that enabled precise epitope mapping for a wide range of B. burgdorferi antigens. In combination with machine learning, this approach facilitated the selection of serologic targets early in disease and the identification of serological indicators associated with different manifestations of Lyme disease. This study provides insights into differential antibody responses during infection and outlines a new approach for improved serologic diagnosis of Lyme disease.