Beyond the antibodies: serum metabolomic profiling of myasthenia gravis
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.
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).
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.
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).
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.
KeywordsMetabolomics Serum Autoimmune Myasthenia gravis Rheumatoid arthritis Immunometabolomics Neuromuscular disease
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.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interests.
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 was obtained from all individual participants included in the study.
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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.CrossRefGoogle Scholar
- 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.CrossRefPubMedPubMedCentralGoogle Scholar
- 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.CrossRefPubMedGoogle Scholar
- 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.CrossRefPubMedPubMedCentralGoogle Scholar
- 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.CrossRefPubMedPubMedCentralGoogle Scholar
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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.CrossRefPubMedGoogle Scholar
- 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.CrossRefPubMedGoogle Scholar
- Pollard, T. D., Thomas, D., Earnshaw, W. C., Lippincott-Schwartz, J., & Johnson, G. T. (2016). Cell biology (3rd ed.). Amsterdam: Elsevier.Google Scholar
- Stuerenburg, H. J. (2000). The roles of carnosine in aging of skeletal muscle and in neuromuscular diseases. Biochemistry (Moscow), 65(7), 862–865.Google Scholar