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.
References
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Hutchinson, D. (1999). Classification criteria: The 1987 American Rheumatism Association revised criteria for the classification of rheumatoid arthritis. CPD Rheumatology, 1(1), 13–14.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
Pollard, T. D., Thomas, D., Earnshaw, W. C., Lippincott-Schwartz, J., & Johnson, G. T. (2016). Cell biology (3rd ed.). Amsterdam: Elsevier.
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.
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.
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.
Stuerenburg, H. J. (2000). The roles of carnosine in aging of skeletal muscle and in neuromuscular diseases. Biochemistry (Moscow), 65(7), 862–865.
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.
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.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
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
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
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11306-019-1571-9
Keywords
- Metabolomics
- Serum
- Autoimmune
- Myasthenia gravis
- Rheumatoid arthritis
- Immunometabolomics
- Neuromuscular disease