Lyme disease is a tick-borne bacterial illness that occurs in areas of North America, Europe, and Asia. Early infection typically presents as generalized symptoms with an erythema migrans (EM) skin lesion. Dissemination of the pathogen Borrelia burgdorferi can result in multiple EM skin lesions or in extracutaneous manifestations such as Lyme neuroborreliosis. Metabolic biosignatures of patients with early Lyme disease can potentially provide diagnostic targets as well as highlight metabolic pathways that contribute to pathogenesis. Sera from well-characterized patients diagnosed with either early localized Lyme disease (ELL) or early disseminated Lyme disease (EDL), plus healthy controls (HC), from the United States were analyzed by liquid chromatography-mass spectrometry (LC-MS). Comparative analyses were performed between ELL, or EDL, or ELL combined with EDL, and the HC to develop biosignatures present in early Lyme disease. A direct comparison between ELL and EDL was also performed to develop a biosignature for stages of early Lyme disease. Metabolic pathway analysis and chemical identification of metabolites with LC-tandem mass spectrometry (LC-MS/MS) demonstrated alterations of eicosanoid, bile acid, sphingolipid, glycerophospholipid, and acylcarnitine metabolic pathways during early Lyme disease. These metabolic alterations were confirmed using a separate set of serum samples for validation. The findings demonstrated that infection of humans with B. burgdorferi alters defined metabolic pathways that are associated with inflammatory responses, liver function, lipid metabolism, and mitochondrial function. Additionally, the data provide evidence that metabolic pathways can be used to mark the progression of early Lyme disease.

Metabolites detectible in human biofluids are attractive biomarkers for the diagnosis of early Lyme disease (ELD), a vector-borne infectious disease. Urine represents an easily obtained clinical sample that can be applied for diagnostic purposes. However, few studies have explored urine for biomarkers of ELD. In this study, metabolomics approaches were applied to evaluate small molecule metabolites in urine from patients with ELD (n = 14), infectious mononucleosis (n = 14) and healthy controls (n = 14). Metabolic biosignatures for ELD versus healthy controls and ELD versus infectious mononucleosis were generated using untargeted metabolomics. Pathway analyses and metabolite identification revealed the dysregulation of several metabolic processes in ELD as compared to healthy controls or mononucleosis, including metabolism of tryptophan. Linear discriminant analyses demonstrated that individual metabolic biosignatures can correctly discriminate ELD from the other patient groups with accuracies of 71 to 100%. These data provide proof-of-concept for use of urine metabolites as biomarkers for diagnostic classification of ELD.

Standard two-tiered testing (STTT) is the recommended algorithm for laboratory diagnosis of Lyme disease (LD). Several limitations are associated with STTT that include low sensitivity in the early stages of disease, as well as technical complexity and subjectivity associated with second-tier immunoblots; therefore, modified two-tiered testing (MTTT) algorithms that utilize two sequential first-tier tests and eliminate immunoblots have been evaluated. Recently, a novel MTTT that uses a VlsE chemiluminescence immunoassay followed by a C6 enzyme immunoassay has been proposed. The purpose of this study was to evaluate the performance of the VlsE/C6 MTTT using well-characterized serum samples. Serum samples from the CDC Lyme Serum Repository were tested using three MTTTs: VlsE/C6, whole cell sonicate (WCS)/C6 and WCS/VlsE, and three STTTs (immunoblots preceded by three different first-tier assays: VlsE, C6 and WCS). Significant differences were not observed between the MTTTs assessed; however, the VlsE/C6 MTTT resulted in the highest specificity (100%) when other diseases were tested and the lowest sensitivity (75%) for LD samples as compared to the other MTTTs evaluated. Significant differences were present between various MTTTs and STTTs evaluated. Specifically, all MTTTs resulted in higher sensitivities for all LD groups combined when compared to the STTTs and were significantly more accurate (i.e. higher proportion of correct classifications) for this group with the exception of the WCS/ViraStripe STTT. Additionally, when other diseases were tested, only the VlsE/C6 MTTT differed significantly from the WCS/ViraStripe STTT with the VlsE/C6 MTTT resulting in a 6.2% higher accuracy. Overall, the VlsE/C6 MTTT offers an additional laboratory testing algorithm for LD with equivalent or enhanced performance to the other MTTTs and STTTs evaluated in this study.

Lyme disease, the most commonly reported vector-borne disease in the United States, results from infection with Borrelia burgdorferi. Early clinical diagnosis of this disease is largely based on the presence of an erythematous skin lesion for individuals in high-risk regions. This, however, can be confused with other illnesses including southern tick-associated rash illness (STARI), an illness that lacks a defined etiological agent or laboratory diagnostic test, and is coprevalent with Lyme disease in portions of the eastern United States. By applying an unbiased metabolomics approach with sera retrospectively obtained from well-characterized patients, we defined biochemical and diagnostic differences between early Lyme disease and STARI. Specifically, a metabolic biosignature consisting of 261 molecular features (MFs) revealed that altered N-acyl ethanolamine and primary fatty acid amide metabolism discriminated early Lyme disease from STARI. Development of classification models with the 261-MF biosignature and testing against validation samples differentiated early Lyme disease from STARI with an accuracy of 85 to 98%. These findings revealed metabolic dissimilarity between early Lyme disease and STARI, and provide a powerful and new approach to inform patient management by objectively distinguishing early Lyme disease from an illness with nearly identical symptoms.

BACKGROUND: Early Lyme disease patients often present to the clinic prior to developing a detectable antibody response to Borrelia burgdorferi, the etiologic agent. Thus, existing two-tier serology-based assays yield low sensitivities (29-40%) for early infection. The lack of an accurate laboratory test for early Lyme disease contributes to misconceptions about diagnosis and treatment, and underscores the need for new diagnostic approaches. METHODS: Retrospective serum samples from patients with early Lyme disease, other diseases, and healthy controls were analyzed for small molecule metabolites by liquid chromatography-mass spectrometry (LC-MS). A metabolomics data workflow was applied to select a biosignature for classifying early Lyme disease and non-Lyme disease patients. A statistical model of the biosignature was trained using the patients' LC-MS data, and subsequently applied as an experimental diagnostic tool with LC-MS data from additional patient sera. The accuracy of this method was compared with standard two-tier serology. RESULTS: Metabolic biosignature development selected 95 molecular features that distinguished early Lyme disease patients from healthy controls. Statistical modeling reduced the biosignature to 44 molecular features, and correctly classified early Lyme disease patients and healthy controls with a sensitivity of 88% (84-95%), and a specificity of 95% (90-100%). Importantly, the metabolic biosignature correctly classified 77-95% of the of serology negative Lyme disease patients. CONCLUSION: The data provide proof-of-concept that metabolic profiling for early Lyme disease can achieve significantly greater (p<0.0001) diagnostic sensitivity than current two-tier serology, while retaining high specificity.

Serological assays and a two-tiered test algorithm are recommended for laboratory confirmation of Lyme disease. In the U.S. the sensitivity of two-tiered testing using commercially available serology-based assays is dependent on the stage of infection and ranges from 30% in the early localized disease to near 100% in late stage disease. Other variables, including subjectivity in reading Western blots, compliance with two-tiered recommendations, use of different first and second tier test combinations, and use of different test samples, all contribute to variation in two-tiered test performance. The availability and use of sample sets from well-characterized Lyme disease patients and controls are needed to better assess the performance of existing tests and for development of improved assays. To address this need, the Centers for Disease Control and Prevention and the National Institutes of Health prospectively collected sera from patients at all stages of Lyme disease, as well as healthy donors and patients with look-alike diseases. Patients and healthy controls were recruited using strict inclusion and exclusion criteria. Samples from all included patients were retrospectively characterized by two-tiered testing. The results from two-tiered testing corroborated the need for novel and improved diagnostics, particularly for laboratory diagnosis of earlier stages of infection. Furthermore, the two-tier results provide a baseline with samples from well-defined patients that can be used in comparing the sensitivity and specificity of novel diagnostics. Panels of sera and accompanying clinical and laboratory testing results are now available to Lyme disease serological test users and researchers developing novel tests.