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.

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 individuals (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

Background Post-treatment Lyme disease symptoms/syndrome (PTLDS) occurs in approximately 10% of Lyme disease patients following antibiotic treatment. Biomarkers or specific clinical symptoms to identify PTLDS patients do not currently exist and the PTLDS classification is based on the report of persistent, subjective symptoms for ≥ 6 months following antibiotic treatment for Lyme disease. Methods Untargeted liquid chromatography-mass spectrometry metabolomics was used to determine longitudinal metabolic responses and biosignatures in PTLDS and clinically cured non-PTLDS Lyme patients. Evaluation of biosignatures included: 1) defining altered classes of metabolites; 2) elastic net regularization to define metabolites that most strongly defined PTLDS and non-PTLDS patients at different timepoints; 3) changes in the longitudinal abundance of metabolites; 4) linear discriminant analysis to evaluate robustness in a second patient cohort. Results This study determined that observable metabolic differences exist between PTLDS and non-PTLDS patients at multiple timepoints. The metabolites with differential abundance included those from glycerophospholipid, bile acid and acylcarnitine metabolism. Distinct longitudinal patterns of metabolite abundance indicated a greater metabolic variability in PTLDS vs non-PTLDS patients. Small numbers of metabolites (6-40) could be used to define PTLDS vs. non-PTLDS patients at defined time points, and the findings were validated in a second cohort of PTLDS and non-PTLDS patients. Conclusions These data provide evidence that an objective metabolite-based measurement can distinguish patients with PTLDS and help understand the underlying biochemistry of PTLDS.

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An untargeted metabolomics approach was utilized to determine urinary metabolites that could serve as small-molecule biomarkers for treatment response to standard tuberculosis treatment. However, the majority of metabolites that most accurately distinguished patient samples at the time of diagnosis from those at 1 month after the start of therapy lacked structural identification. The detection of unknown metabolite structures is a well-known limitation of untargeted metabolomics and underscores a need for continued elucidation of novel metabolite structures. In this study, we sought to define the structure of a urine metabolite with an experimentally determined mass of 202.1326 Da, classified as molecular feature (MF) 202.1326. A hypothesized structure of N1-acetylisoputreanine was developed for MF 202.1326 using in silico tools and liquid chromatography–tandem mass spectrometry (LC–MS/MS). In the absence of a commercial standard, synthetic N1-acetylisoputreanine was generated using enzymatic and chemical syntheses, and LC–MS/MS was used to confirm the structure of MF 202.1326 as N1-acetylisoputreanine, a proposed terminal polyamine catabolite that had not been previously detected in biological samples. Further analysis demonstrated that N1-acetylisoputreanine and an alternative form of this metabolite, N1-acetylisoputreanine-γ-lactam, are both present in human urine and are likely end-products of polyamine metabolism.