. 2017 Aug 16;9(403):eaal2717.
Metabolic Differentiation of Early Lyme Disease From Southern Tick-Associated Rash Illness (STARI)
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Metabolic Differentiation of Early Lyme Disease From Southern Tick-Associated Rash Illness (STARI)
Sci Transl Med
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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.
Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
Conflict of interest statement
Competing interests: Dr. Wormser reports receiving research grants from Immunetics, Inc., Institute for Systems Biology, Rarecyte, Inc., and Quidel Corporation. He owns equity in Abbott; has been an expert witness in malpractice cases involving Lyme disease; and is an unpaid board member of the American Lyme Disease Foundation.
Fig. 1. Metabolic profiling for the identification and application of differentiating molecular features (MFs)
A) LC-MS data from an initial Discovery-Set of early Lyme disease (EL) and STARI samples was used to identify a list of MFs that were targeted in a second LC-MS run. The data from both LC-MS runs was combined to form the Targeted-Discovery-Set. The MFs were then screened for consistency and robustness and this resulted in a final early Lyme disease-STARI biosignature of 261 MFs. This biosignature was used for downstream pathway analysis and for classification modeling. ( B) Two training-data sets along with the 261 MF biosignature list were used to train multiple classification models, random forest (RF) and least absolute shrinkage and selection operator (LASSO). Data from samples of twoTest-Sets (not included for the Discovery/Training-Set data) were blindly tested against the two-way (EL vs STARI) and three-way [EL vs STARI vs healthy controls (HC)] classification models. The regression coeficients used for each MF in the LASSO two-way and three-way classification models are provided in table S4 and S6, resepectively.
Fig. 2. Pathways differentially regulated in patients with early Lyme disease and STARI
The 122 presumptively identified MFs were analyzed using MetaboAnalyst to identify perturbed pathways between early Lyme disease and STARI. The color and size of each circle is based on P values and pathway impact values. Pathways with a > 0.1 impact were considered to be perturbed and differentially regulated between patients with early Lyme disease and STARI. A total of four pathways were affected: 1) glycerophospholipid metabolism; 2) sphingolipid metabolism; 3) valine, leucine and isoleucine biosynthesis; and 4) phenylalanine metabolism.
Fig. 3. Metabolite identification and association with NAE and PFAM metabolism
Structural identification of palmitoyl ethanolamide (
A and B) and other NAEs (fig S1–S3) in the 261 MF biosignature indicated alteration of NAE metabolism ( C), a pathway that can influence the production of PFAMs. Further MF identification revealed that palmitamide ( D and E) and other PFAMs (fig S5 and S6) also differed in abundance between STARI and early Lyme disease patients. Structural identification was achieved by retention-time alignment ( A and D) of authentic standard (top panel), authentic standard spiked in pooled patient sera (middle panel), and the targeted metabolite in pooled patient sera (bottom panel), and by comparison of MS/MS spectra ( B and E) of the authentic standards (top) and the targeted metabolites in patient sera (bottom). Retention-time alignments for palmitoyl ethanolamide (A) and palmitamide (D) were generated with extracted ion chromatograms for m/z 300.2892 and m/z 256.2632, respectively. The relationship of PFAM formation to NAE metabolism is highlighted in light green in diagram C. PLA, phospholipase A; PLC, phospholipase C; PLD, phospholipase D; ADH, alcohol dehydrogenase; PAM, peptidylglycine α-amidating monooxygenase.
Fig. 4. Comparison of MF abundances from the Lyme disease-STARI biosignature against healthy controls
(A) Fourteen of the metabolites with level 1 or level 2 structural identifications were evaluated for abundance differences between early Lyme disease (green squares) and STARI (blue triangles) normalized to the metabolite abundance in healthy controls. Included are metabolites identified for NAE and PFAM metabolism. GP-NAE: glycerophospho-
N-palmitoyl ethanolamine; Lyso PA (20:4): arachidonoyl lysophosphatidic acid; CMPF: 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid. The relative mean abundance and 95% confidence intervals are shown for each metabolite. (B) Abundance fold change ranges (x-axis) plotted against the percent of MFs from the 261 MF early Lyme disease-STARI biosignature that have increased (dark blue) or decreased (light blue) abundances in STARI relative to healthy controls, and increased (dark green) or decreased (light green) abundances in early Lyme disease relative to healthy controls. (C) The percentage of identical MFs in STARI and early Lyme disease that had the same directional and similar abundance fold change difference relative to healthy controls (y-axis). MFs were grouped based on abundance fold change ranges:1.0–1.4, 1.5–1.9, 2.0–2.4, 2.5–2.9, 3.0–3.4, and ≥3.5 (x-axis). MFs with increased fold changes relative to healthy controls are indicated in dark purple or and those with a decreased fold changes are indicated in light purple.
Fig. 5. Evaluation of classification models’ performance
A) LASSO scores (Xβ; i.e. the linear portion of the regression model) were calculated for Test-Set data of early Lyme disease (green dots) and STARI (blue triangles) serum samples by multiplying the transformed abundances of the 38 MFs identified in the two-way LASSO model by the LASSO coefficients of the model and summing for each sample. Scores are plotted along the y-axis; serum samples are plotted randomly along the x-axis for easier viewing. ( B) An ROC curve demonstrates the level of discrimination that is achieved between early Lyme disease and STARI using the 38 MFs of the two-way LASSO classification model by depicting a true positive rate (sensitivity; early Lyme disease) versus a false positive rate (specificity; STARI) for the Test-Set samples (table S6). The AUC was calculated to be 0.986. The diagonal line represents an AUC value of 0.5. The performance of two-tiered testing (red dot) on the same sample set (Test-Set 1) was included as a reference for the sensitivity and specificity of the current clinical laboratory test for Lyme disease. ( C) LASSO scores (Xβi) were calculated for the Test-Set data of early Lyme disease (green spheres), STARI (blue spheres), and healthy control (black spheres) serum samples by multiplying the transformed abundances of the 82 MFs identified in the three-way LASSO model by each of three LASSO coefficients used in the model. Each axis represents the sample score in the direction of one of the three sample groups. Scores are used in calculation of probabilities of class membership, with highest probability determining the predicted class. Although there is overlap, the three groups predominantly occupy distinct areas of the plot.
Fig. 6. Evaluation of intra-and inter-group variability
Linear discriminant analysis was performed using the 82 MFs picked by LASSO in the three-way classification model to assess the intra-group variability based on the geographical region or laboratory from which STARI (CO-blue, solid; FL-green, dotted; and NY-red, dashed) and healthy control (MO-dark blue, solid; NC-light blue, dotted; and Other-green, dashed) sera were obtained. Only slight intra-group variation was observed. This analysis also compared and showed clear differentiation of all healthy control from STARI samples regardless of geographical region or laboratory origin. Healthy controls from FL were included in this analysis to demonstrate that healthy controls from an area with low incidence of Lyme disease and where STARI cases occur do not differ from the healthy controls obtained from other regions and used in the classification modeling.
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Exanthema / parasitology*
Lyme Disease / classification
Lyme Disease / diagnosis*
Lyme Disease / metabolism*
Metabolic Networks and Pathways
Tick Infestations / blood
Tick Infestations / classification
Tick Infestations / diagnosis*
Tick Infestations / metabolism*