Beyond the antibodies: serum metabolomic profiling of myasthenia gravis

Abstract

Introduction

Myasthenia gravis (MG) is a chronic, potentially debilitating autoimmune disease characterized by weakness and rapid fatigue of the voluntary muscles that worsens on exertion. Left untreated, MG symptoms may cause significant morbidity or even death. To date, no robust biological marker is available to follow the course of the disease. Therefore, new diagnostic approaches and biological markers are essential not only for improved diagnosis of the disease but for improved outcomes.

Objectives

The present study applied a two-control, multi-label metabolomics profiling approach as a potential strategy for the identification of biomarkers unique to myasthenia gravis (MG).

Methods

Metabolic analyses using acid- and dansyl-labelled serum from seropositive MG (n = 46), rheumatoid arthritis (RA) (n = 23) and healthy controls (HC) (n = 49) were performed on samples from adult patients presenting to the University of Alberta Hospital neuromuscular and rheumatology clinics. Comparisons between patients with MG vs. HC, and RA vs. HC were made using univariate and multivariate statistics.

Results

Serum biomarker patterns were statistically significantly different between groups. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models exhibited considerable distinction between all groups. Metabolites were then filtered to remove peak pairs common to both disease cohorts. Combined metabolite panels revealed clear separation between MG and HC for both library-matched (AUROC: 0.92 ± 0.03) and highest AUC patients (AUROC: 0.94 ± 0.05).

Conclusion

In patients presenting to the clinic with seropositive MG, metabolomic profiling is capable of distinguishing patients with disease from those without. These results provide an important first step towards a potential biomarker for improving MG identification.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Adamczyk-Sowa, M., Bieszczad-Bedrejczuk, E., Galiniak, S., Rozmiłowska, I., Czyżewski, D., Bartosz, G., et al. (2017). Oxidative modifications of blood serum proteins in myasthenia gravis. Journal of Neuroimmunology, 305, 145–153. https://doi.org/10.1016/j.jneuroim.2017.01.019.

    CAS  Article  PubMed  Google Scholar 

  2. Astudillo, A. M., Balgoma, D., Balboa, M. A., & Balsinde, J. (2012). Dynamics of arachidonic acid mobilization by inflammatory cells. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids, 1821(2), 249–256. https://doi.org/10.1016/j.bbalip.2011.11.006.

    CAS  Article  Google Scholar 

  3. Bass JD, D. A., & R. D. (2015). qvalue: Q-value estimation for false discovery rate control. R package version 2.10.0. http://github.com/jdstorey/qvalue

  4. Benatar, M., Sanders, D. B., Burns, T. M., Cutter, G. R., Guptill, J. T., Baggi, F., et al. (2012). Recommendations for myasthenia gravis clinical trials. Muscle and Nerve, 45(6), 909–917. https://doi.org/10.1002/mus.23330.

    Article  PubMed  Google Scholar 

  5. Boothby, M., & Rickert, R. C. (2017). Metabolic regulation of the immune humoral response. Immunity, 46(5), 743–755. https://doi.org/10.1016/j.immuni.2017.04.009.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Cobb, J., Eckhart, A., Motsinger-Reif, A., Carr, B., Groop, L., & Ferrannini, E. (2016). α-hydroxybutyric acid is a selective metabolite biomarker of impaired glucose tolerance. Diabetes Care, 39(6), 988–995. https://doi.org/10.2337/dc15-2752.

    CAS  Article  PubMed  Google Scholar 

  7. Cocco, E., Murgia, F., Lorefice, L., Barberini, L., Poddighe, S., Frau, J., et al. (2016). (1)H-NMR analysis provides a metabolomic profile of patients with multiple sclerosis. Neurology(R) Neuroimmunology & Neuroinflammation, 3(1), e185. https://doi.org/10.1212/nxi.0000000000000185.

    Article  Google Scholar 

  8. Cummings, B. S., McHowat, J., & Schnellmann, R. G. (2000). Phospholipase A2s in Cell Injury and Death. Journal of Pharmacology and Experimental Therapeutics, 294(3), 793–799.

    CAS  PubMed  Google Scholar 

  9. Ferrannini, E., Natali, A., Camastra, S., Nannipieri, M., Mari, A., Adam, K.-P., et al. (2013). Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes, 62(5), 1730–1737. https://doi.org/10.2337/db12-0707.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Franken, C., Lambrechts, N., Govarts, E., Koppen, G., Den Hond, E., Ooms, D., et al. (2017). Phthalate-induced oxidative stress and association with asthma-related airway inflammation in adolescents. International Journal of Hygiene and Environmental Health, 220(2), 468–477. https://doi.org/10.1016/J.IJHEH.2017.01.006.

    CAS  Article  PubMed  Google Scholar 

  11. Gall, W. E., Beebe, K., Lawton, K. A., Adam, K.-P., Mitchell, M. W., Nakhle, P. J., et al. (2010a). α-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE, 5(5), e10883. https://doi.org/10.1371/journal.pone.0010883.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Gall, W. E., Beebe, K., Lawton, K. A., Adam, K. P., Mitchell, M. W., Nakhle, P. J., et al. (2010b). α-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE, 5(5), e10883. https://doi.org/10.1371/journal.pone.0010883.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., et al. (2004). Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology, 5(10), R80. https://doi.org/10.1186/gb-2004-5-10-r80.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Guptill, J. T., & Sanders, D. B. (2010). Update on muscle-specific tyrosine kinase antibody positive myasthenia gravis. Current Opinion in Neurology, 23(5), 530–535. https://doi.org/10.1097/WCO.0b013e32833c0982.

    CAS  Article  PubMed  Google Scholar 

  15. Hoffmann, G. F., Meier-Augenstein, W., Stöckler, S., Surtees, R., Rating, D., & Nyhan, W. L. (1993). Physiology and pathophysiology of organic acids in cerebrospinal fluid. Journal of Inherited Metabolic Disease, 16(4), 648–669. https://doi.org/10.1007/BF00711898.

    CAS  Article  PubMed  Google Scholar 

  16. Howard, F. M., Lennon, V. A., Finley, J., Matsumoto, J., & Elveback, L. R. (1987). Clinical correlations of antibodies that bind, block, or modulate human acetylcholine receptors in myasthenia gravis. Annals of the New York Academy of Sciences, 505, 526–538.

    Article  Google Scholar 

  17. Huan, T., Tang, C., Li, R., Shi, Y., Lin, G., & Li, L. (2015). MyCompoundID MS/MS search: Metabolite identification using a library of predicted fragment-ion-spectra of 383,830 possible human metabolites. Analytical Chemistry, 87(20), 10619–10626. https://doi.org/10.1021/acs.analchem.5b03126.

    CAS  Article  PubMed  Google Scholar 

  18. Hutchinson, D. (1999). Classification criteria: The 1987 American Rheumatism Association revised criteria for the classification of rheumatoid arthritis. CPD Rheumatology, 1(1), 13–14.

    Google Scholar 

  19. Kaminski, H. J., Kusner, L. L., Wolfe, G. I., Aban, I., Minisman, G., Conwit, R., et al. (2012). Biomarker development for myasthenia gravis. Annals of the New York Academy of Sciences, 1275(1), 101–106. https://doi.org/10.1111/j.1749-6632.2012.06787.x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Lassmann, H., Van Horssen, J., & Mahad, D. (2012). Progressive multiple sclerosis: Pathology and pathogenesis. Nature Reviews Neurology, 8(11), 647–656. https://doi.org/10.1038/nrneurol.2012.168.

    CAS  Article  PubMed  Google Scholar 

  21. Lin, M. T., & Beal, M. F. (2006). Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature, 443(7113), 787–795. https://doi.org/10.1038/nature05292.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Lindahl, A., Forshed, J., & Nordström, A. (2016). Overlap in serum metabolic profiles between non-related diseases: Implications for LC-MS metabolomics biomarker discovery. Biochemical and Biophysical Research Communications, 478(3), 1472–1477. https://doi.org/10.1016/J.BBRC.2016.08.155.

    CAS  Article  PubMed  Google Scholar 

  23. Loftus, R. M., & Finlay, D. K. (2016). Immunometabolism: Cellular metabolism turns immune regulator. The Journal of biological chemistry, 291(1), 1–10. https://doi.org/10.1074/jbc.R115.693903.

    CAS  Article  PubMed  Google Scholar 

  24. Lone, A. M., & Taskén, K. (2013). Proinflammatory and immunoregulatory roles of eicosanoids in T cells. Frontiers in Immunology, 4, 130. https://doi.org/10.3389/fimmu.2013.00130.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Lu, Y., Wang, C., Chen, Z., Zhao, H., Chen, J., Liu, X., et al. (2012). Serum metabolomics for the diagnosis and classification of myasthenia gravis. Metabolomics, 8(4), 704–713. https://doi.org/10.1007/s11306-011-0364-6.

    CAS  Article  Google Scholar 

  26. Lunt, S. Y., & Vander Heiden, M. G. (2011). Aerobic glycolysis: Meeting the metabolic requirements of cell proliferation. Annual Review of Cell and Developmental Biology, 27(1), 441–464. https://doi.org/10.1146/annurev-cellbio-092910-154237.

    CAS  Article  PubMed  Google Scholar 

  27. Manley, K., Han, W., Zelin, G., & Lawrence, D. A. (2018). Crosstalk between the immune, endocrine, and nervous systems in immunotoxicology. Current Opinion in Toxicology, 10, 37–45. https://doi.org/10.1016/J.COTOX.2017.12.003.

    Article  Google Scholar 

  28. Meriggioli, M. N., & Sanders, D. B. (2012). Muscle autoantibodies in myasthenia gravis: Beyond diagnosis? Expert review of clinical immunology, 8(5), 427–438. https://doi.org/10.1586/eci.12.34.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Narang, A. S., & Boddu, S. H. S. (Eds.). (2015). Excipient applications in formulation design and drug delivery. Cham: Springer. https://doi.org/10.1007/978-3-319-20206-8

    Google Scholar 

  30. Nguyen, A., & Bouscarel, B. (2008). Bile acids and signal transduction: Role in glucose homeostasis. Cellular Signalling, 20(12), 2180–2197. https://doi.org/10.1016/J.CELLSIG.2008.06.014.

    CAS  Article  PubMed  Google Scholar 

  31. Park, H., Bourla, A. B., Kastner, D. L., Colbert, R. A., & Siegel, R. M. (2012). Lighting the fires within: The cell biology of autoinflammatory diseases. Nature Reviews Immunology, 12(8), 570–580. https://doi.org/10.1038/nri3261.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Pollard, T. D., Thomas, D., Earnshaw, W. C., Lippincott-Schwartz, J., & Johnson, G. T. (2016). Cell biology (3rd ed.). Amsterdam: Elsevier.

    Google Scholar 

  33. Sengupta, M., Cheema, A., Kaminski, H. J., Kusner, L. L., Gribbestad, I., et al. (2014). serum metabolomic response of myasthenia gravis patients to chronic prednisone treatment. PLoS ONE, 9(7), e102635. https://doi.org/10.1371/journal.pone.0102635.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Staley, C., Weingarden, A. R., Khoruts, A., & Sadowsky, M. J. (2017). Interaction of gut microbiota with bile acid metabolism and its influence on disease states. Applied Microbiology and Biotechnology, 101(1), 47–64. https://doi.org/10.1007/s00253-016-8006-6.

    CAS  Article  PubMed  Google Scholar 

  35. Storey, J. D. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. The Annals of Statistics, 31(6), 2013–2035. https://doi.org/10.1214/aos/1074290335.

    Article  Google Scholar 

  36. Stuerenburg, H. J. (2000). The roles of carnosine in aging of skeletal muscle and in neuromuscular diseases. Biochemistry (Moscow), 65(7), 862–865.

    CAS  Google Scholar 

  37. Tomiko, K., Toshihiro, S., Yoshito, I., Masahiro, M., Makoto, Y., Yusuke, S., et al. (1983). Studies of urinary organic acid profiles of a patient with dihydrolipoyl dehydrogenase deficiency. Clinica Chimica Acta, 133(2), 133–140. https://doi.org/10.1016/0009-8981(83)90398-4.

    Article  Google Scholar 

  38. Win-Shwe, T.-T., Yanagisawa, R., Koike, E., Nitta, H., & Takano, H. (2013). Expression levels of neuroimmune biomarkers in hypothalamus of allergic mice after phthalate exposure. Journal of Applied Toxicology, 33(10), 1070–1078. https://doi.org/10.1002/jat.2835.

    CAS  Article  PubMed  Google Scholar 

  39. Xia, J., Mandal, R., Sinelnikov, I. V., Broadhurst, D., & Wishart, D. S. (2012). MetaboAnalyst 2.0–a comprehensive server for metabolomic data analysis. Nucleic Acids Research, 40(W1), W127–W133. https://doi.org/10.1093/nar/gks374.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Yegambaram, M., Manivannan, B., Beach, T. G., & Halden, R. U. (2015). Role of environmental contaminants in the etiology of Alzheimer’s disease: A review. Current Alzheimer Research, 12(2), 116–146. https://doi.org/10.2174/1567205012666150204121719.

    CAS  Article  PubMed  Google Scholar 

  41. Zhao, S., Luo, X., & Li, L. (2016). Chemical isotope labeling LC-MS for high coverage and quantitative profiling of the hydroxyl submetabolome in metabolomics. Analytical Chemistry, 88(21), 10617–10623. https://doi.org/10.1021/acs.analchem.6b02967.

    CAS  Article  PubMed  Google Scholar 

  42. Zhou, R., Tseng, C.-L., Huan, T., & Li, L. (2014). IsoMS: automated processing of LC-MS data generated by a chemical isotope labeling metabolomics platform. Analytical Chemistry, 86(10), 4675–4679. https://doi.org/10.1021/ac5009089.

    CAS  Article  PubMed  Google Scholar 

  43. Zhu, C., Fuchs, C. D., Halilbasic, E., & Trauner, M. (2016). Bile acids in regulation of inflammation and immunity: Friend or foe? Clinical and Experimental Rheumatology, 34(4 Suppl 98), 25–31.

    PubMed  Google Scholar 

Download references

Author information

Affiliations

Authors

Contributions

DB developed the concept and designed the experiment, acquired all blood samples but those from rheumatoid arthritis patients, conducted all databasing, bioinformatics analyses and metabolite-database matching. Further, DB prepared all charts, images and tables and wrote the paper. ZS provided project oversight as the clinical expert in myasthenia gravis. ZS also offered conceptual advice, supervised project analysis and edited the manuscript. LL developed the chemical labelling process and several chemometric analysis tools used in this study as well as the chemical identification libraries used in the positive identification of observed metabolites. LL provided oversight in the choice of chemical and statistical techniques used to characterize the metabolome. LL also offered conceptual advice, supervised project analysis and edited the manuscript. NW performed the sample preparation, chemical labelling and mass spectrometric analysis. WM provided the rheumatoid arthritis samples for the disease control cohort. All authors reviewed the manuscript.

Corresponding author

Correspondence to Derrick Blackmore.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests.

Human research

This work received ethics approval from the University of Alberta Research Ethics Board and is in compliance with the ethical standards of this institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Blackmore, D., Siddiqi, Z., Li, L. et al. Beyond the antibodies: serum metabolomic profiling of myasthenia gravis. Metabolomics 15, 109 (2019). https://doi.org/10.1007/s11306-019-1571-9

Download citation

Keywords

  • Metabolomics
  • Serum
  • Autoimmune
  • Myasthenia gravis
  • Rheumatoid arthritis
  • Immunometabolomics
  • Neuromuscular disease