Research ArticleInfectious diseases

Metabolic differentiation of early Lyme disease from southern tick–associated rash illness (STARI)

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Science Translational Medicine  16 Aug 2017:
Vol. 9, Issue 403, eaal2717
DOI: 10.1126/scitranslmed.aal2717
  • Fig. 1. Metabolic profiling for the identification and application of differentiating MFs.

    (A) LC-MS data from an initial Discovery-Set of early Lyme disease (EL) and STARI samples were used to identify a list of MFs that were targeted in a second LC-MS run. The data from both LC-MS runs were 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. MPP, Mass Profiler Pro. (B) Two training-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 two Test-Sets (not included for the Discovery/Training-Set data) were blindly tested against the two-way (early Lyme disease versus STARI) and three-way [early Lyme disease versus STARI versus healthy controls (HC)] classification models. The regression coefficients used for each MF in the LASSO two-way and three-way classification models are provided in tables S4 and S6, respectively.

  • 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 are 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: (i) glycerophospholipid metabolism; (ii) sphingolipid metabolism; (iii) valine, leucine, and isoleucine biosynthesis; and (iv) phenylalanine metabolism.

  • Fig. 3. Metabolite identification and association with NAE and PFAM metabolism.

    Structural identification of palmitoyl ethanolamide (A and B) and palmitamide (D and E) in the 261-MF biosignature is part of NAE and PFAM metabolism (C). Structural identification was achieved by RT 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). RT alignments for palmitoyl ethanolamide (A) and palmitamide (D) were generated with extracted ion chromatograms for m/z of 300.2892 and 256.2632, respectively. The relationship of PFAM formation to NAE metabolism is highlighted in light green in (C). PLA, phospholipase A; PLC, phospholipase C; PLD, phospholipase D; ADH, alcohol dehydrogenase; PAM, peptidylglycine α-amidating monooxygenase; AEA, arachidonoyl ethanolamide.

  • 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. Metabolites identified for NAE and PFAM metabolism are included. GP-NPEA, glycerophospho-N-palmitoyl ethanolamine. 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) 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 on the basis of abundance fold change ranges: 1.0 to 1.4, 1.5 to 1.9, 2.0 to 2.4, 2.5 to 2.9, 3.0 to 3.4, and ≥3.5 (x axis). MFs with increased fold changes relative to healthy controls are indicated in dark purple, and those with decreased fold changes are indicated in light purple.

  • Fig. 5. Evaluation of classification models’ performance.

    (A) LASSO scores (Xβ, that is, 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.

  • Fig. 6. Evaluation of intra- and intergroup variability.

    Linear discriminant analysis was performed using the 82 MFs picked by LASSO in the three-way classification model to assess the intragroup variability based on the geographical region or laboratory from which healthy control (CO, blue, solid; FL, green, dotted; and NY, red, dashed) and STARI (MO, dark blue, solid; NC, light blue, dotted; and Other, green, dashed) sera were obtained.

  • Table 1. Classification modeling using the 261-MF biosignature list.
    Classification
    model
    Test-Set sample
    group
    Number of data
    files*
    RF (261 MFs)LASSO (38/82 MFs)
    Number correctly
    predicted
    % Classification
    accuracy
    Number correctly
    predicted
    % Classification
    accuracy
    1. Two-way modelEarly Lyme disease6058975998
    STARI3834893489
    2. Three-way modelEarly Lyme disease6051855185
    STARI3835923592
    Healthy controls4038953793

    *Samples were analyzed in duplicate by LC-MS.

    †A total of 38 MFs were selected by the LASSO model for two-way modeling, and 82 MFs were selected by the LASSO model for three-way modeling.

    Supplementary Materials

    • www.sciencetranslationalmedicine.org/cgi/content/full/9/403/eaal2717/DC1

      Fig. S1. Level 1 identification of stearoyl ethanolamide.

      Fig. S2. Level 1 identification of pentadecanoyl ethanolamide.

      Fig. S3. Level 1 identification of eicosanoyl ethanolamide.

      Fig. S4. Level 1 identification of glycerophospho-N-palmitoyl ethanolamine.

      Fig. S5. Level 1 identification of stearamide.

      Fig. S6. Level 1 identification of erucamide.

      Fig. S7. Level 1 identification of l-phenylalanine.

      Fig. S8. Level 1 identification of nonanedioic acid.

      Fig. S9. Level 1 identification of glycocholic acid.

      Fig. S10. Level 1 identification of CMPF.

      Fig. S11. Level 2 identification of Lyso PA (20:4) by MS/MS spectral matching.

      Fig. S12. Level 2 identification of 3-ketosphingosine by MS/MS.

      Table S1. Serum samples used in this study.

      Table S2. 261-MF biosignature list.

      Table S3. MetaboAnalyst results.

      Table S4. Regression coefficients (β) of the LASSO two-way statistical model.

      Table S5. LASSO and RF two-way model classification probability scores.

      Table S6. Regression coefficients (β) of the LASSO three-way statistical model.

      Table S7. LASSO and RF three-way model classification probability scores.

      Table S8. ANOVA results on LASSO and RF scores with sample source as the explanatory variable.

      Table S9. Grouping indicated by the ANOVA results.

    • Supplementary Material for:

      Metabolic differentiation of early Lyme disease from southern tick–associated rash illness (STARI)

      Claudia R. Molins, Laura V. Ashton, Gary P. Wormser, Barbara G. Andre, Ann M. Hess, Mark J. Delorey, Mark A. Pilgard, Barbara J. Johnson, Kristofor Webb, M. Nurul Islam, Adoracion Pegalajar-Jurado, Irida Molla, Mollie W. Jewett, John T. Belisle*

      *Corresponding author. Email: john.belisle{at}colostate.edu

      Published 16 August 2017, Sci. Transl. Med. 9, eaal2717 (2017)
      DOI: 10.1126/scitranslmed.aal2717

      This PDF file includes:

      • Fig. S1. Level 1 identification of stearoyl ethanolamide.
      • Fig. S2. Level 1 identification of pentadecanoyl ethanolamide.
      • Fig. S3. Level 1 identification of eicosanoyl ethanolamide.
      • Fig. S4. Level 1 identification of glycerophospho-N-palmitoyl ethanolamine.
      • Fig. S5. Level 1 identification of stearamide.
      • Fig. S6. Level 1 identification of erucamide.
      • Fig. S7. Level 1 identification of ʟ-phenylalanine.
      • Fig. S8. Level 1 identification of nonanedioic acid.
      • Fig. S9. Level 1 identification of glycocholic acid.
      • Fig. S10. Level 1 identification of CMPF.
      • Fig. S11. Level 2 identification of Lyso PA (20:4) by MS/MS spectral matching.
      • Fig. S12. Level 2 identification of 3-ketosphingosine by MS/MS.
      • Table S1. Serum samples used in this study.
      • Table S2. 261-MF biosignature list.
      • Table S3. MetaboAnalyst results.
      • Table S4. Regression coefficients (β) of the LASSO two-way statistical model.
      • Table S5. LASSO and RF two-way model classification probability scores.
      • Table S6. Regression coefficients (β) of the LASSO three-way statistical model.
      • Table S7. LASSO and RF three-way model classification probability scores.
      • Table S8. ANOVA results on LASSO and RF scores with sample source as the explanatory variable.
      • Table S9. Grouping indicated by the ANOVA results.

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