Skip to main content

Advertisement

SpringerLink
Log in

Serum Exosome MicroRNAs Predict Multiple Sclerosis Disease Activity after Fingolimod Treatment

  • Published:
Molecular Neurobiology Aims and scope Submit manuscript

Abstract

We and others have previously demonstrated the potential for circulating exosome microRNAs to aid in disease diagnosis. In this study, we sought the possible utility of serum exosome microRNAs as biomarkers for disease activity in multiple sclerosis patients in response to fingolimod therapy. We studied patients with relapsing-remitting multiple sclerosis prior to and 6 months after treatment with fingolimod. Disease activity was determined using gadolinium-enhanced magnetic resonance imaging. Serum exosome microRNAs were profiled using next-generation sequencing. Data were analysed using univariate/multivariate modelling and machine learning to determine microRNA signatures with predictive utility. Accordingly, we identified 15 individual miRNAs that were differentially expressed in serum exosomes from post-treatment patients with active versus quiescent disease. The targets of these microRNAs clustered in ontologies related to the immune and nervous systems and signal transduction. While the power of individual microRNAs to predict disease status post-fingolimod was modest (average 77%, range 65 to 91%), several combinations of 2 or 3 miRNAs were able to distinguish active from quiescent disease with greater than 90% accuracy. Further stratification of patients identified additional microRNAs associated with stable remission, and a positive response to fingolimod in patients with active disease prior to treatment. Overall, these data underscore the value of serum exosome microRNA signatures as non-invasive biomarkers of disease in multiple sclerosis and suggest they may be used to predict response to fingolimod in future clinical practice. Additionally, these data suggest that fingolimod may have mechanisms of action beyond its known functions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The RNA-seq profile of exosomal miRNAs produced in this study is available at GEO repository with accession number GSE140106.

References

  1. Milo R, Miller A (2014) Revised diagnostic criteria of multiple sclerosis. Autoimmun Rev 13(4–5):518–524

    Article  PubMed  Google Scholar 

  2. Thompson AJ et al (2018) Multiple sclerosis. Lancet 391(10130):14

    Article  Google Scholar 

  3. Cree BA, Mares J, Hartung H-P (2019) Current therapeutic landscape in multiple sclerosis: an evolving treatment paradigm. 32(3):365–377

  4. Ciccarelli OJTLN (2019) Multiple sclerosis in 2018: new therapies and biomarkers. 18(1):10–12

    Article  PubMed  Google Scholar 

  5. English C, Aloi JJ (2015) New FDA-approved disease-modifying therapies for multiple sclerosis. Clin Ther 37(4):691–715

    Article  CAS  PubMed  Google Scholar 

  6. Fonseca J (2015) Fingolimod real world experience: efficacy and safety in clinical practice

    Article  CAS  Google Scholar 

  7. Chun J et al (2019) Fingolimod: lessons learned and new opportunities for treating multiple sclerosis and other disorders. 59:149–170

  8. Ebrahimkhani S et al (2017) Exosomal microRNA signatures in multiple sclerosis reflect disease status. Sci Rep 7(1):14293

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. De Felice B et al (2014) Small non-coding RNA signature in multiple sclerosis patients after treatment with interferon-β. BMC Med Genet 7(1):26

    Google Scholar 

  10. Junker A, Hohlfeld R, Meinl E (2011) The emerging role of microRNAs in multiple sclerosis. Nat Rev Neurol 7(1):56–59

    Article  CAS  PubMed  Google Scholar 

  11. Fenoglio C et al (2016) Effect of fingolimod treatment on circulating miR-15b, miR23a and miR-223 levels in patients with multiple sclerosis. J Neuroimmunol 299:81–83

    Article  CAS  PubMed  Google Scholar 

  12. Waschbisch A et al (2011) Glatiramer acetate treatment normalizes deregulated microRNA expression in relapsing remitting multiple sclerosis. PLoS One 6(9):e24604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gandhi R (2015) miRNA in multiple sclerosis: search for novel biomarkers. Mult Scler 21(9):1095–1103

    Article  CAS  PubMed  Google Scholar 

  14. Hecker M et al (2013) MicroRNA expression changes during interferon-beta treatment in the peripheral blood of multiple sclerosis patients. Int J Mol Sci 14(8):16087–16110

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Ingwersen J et al (2015) Natalizumab restores aberrant miRNA expression profile in multiple sclerosis and reveals a critical role for miR-20b. Ann Clin Transl Neurol 2(1):43–55

    Article  CAS  PubMed  Google Scholar 

  16. Polman CH et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33(11):1444–1444

    Article  CAS  PubMed  Google Scholar 

  18. Kaunzner UW, Gauthier SA (2017) MRI in the assessment and monitoring of multiple sclerosis: an update on best practice. Ther Adv Neurol Disord 10(6):247–261

    Article  PubMed  PubMed Central  Google Scholar 

  19. Giorgio A, De Stefano N (2018) Effective utilization of MRI in the diagnosis and management of multiple sclerosis. Neurol Clin 36(1):27–34

    Article  PubMed  Google Scholar 

  20. Filippi M, Preziosa P, Rocca MA (2014) Magnetic resonance outcome measures in multiple sclerosis trials: time to rethink? Curr Opin Neurol 27(3):290–299

    Article  PubMed  Google Scholar 

  21. Filippi M et al (2019) Association between pathological and MRI findings in multiple sclerosis. 18(2):198–210

  22. Lotvall J et al (2014) Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the International Society for Extracellular Vesicles. J Extracell Vesicles 3:26913

    Article  PubMed  Google Scholar 

  23. Charity W, Law YC, Shi W, Smyth GK (2014) voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29

    Article  CAS  Google Scholar 

  24. Liu R et al (2015) Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res 43(15):e97

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:3

    Article  Google Scholar 

  26. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics:837–845

  27. Tukey JW (1977) Exploratory data analysis. Vol. 2: Reading, Mass

  28. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  29. Liberzon A et al (2015) The molecular signatures database hallmark gene set collection. Cell Syst 1(6):417–425

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ru Y et al (2014) The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations. Nucleic Acids Res 42(17):e133–e133

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Ludwig N et al (2016) Distribution of miRNA expression across human tissues. 44(8):3865–3877

  32. Kappos L et al (2010) A placebo-controlled trial of oral fingolimod in relapsing multiple sclerosis. N Engl J Med 362(5):387–401

    Article  CAS  PubMed  Google Scholar 

  33. Kanehisa M et al (2016) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(D1):D353–D361

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Fabregat A et al (2017) The reactome pathway knowledgebase. Nucleic Acids Res 46(D1):D649–D655

    Article  PubMed Central  CAS  Google Scholar 

  35. Prager B, Spampinato SF, Ransohoff RM (2015) Sphingosine 1-phosphate signalling at the blood-brain barrier. Trends Mol Med 21(6):354–363

    Article  CAS  PubMed  Google Scholar 

  36. Pistono C et al (2017) What’s new about oral treatments in multiple sclerosis? Immunogenetics still under question. Pharmacol Res 120:279–293

    Article  CAS  PubMed  Google Scholar 

  37. Meira M et al (2014) Unravelling natalizumab effects on deregulated miR-17 expression in CD4+ T cells of patients with relapsing-remitting multiple sclerosis. J Immunol Res 2014:897249

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Muñoz-Culla M, Irizar H, Castillo-Triviño T, Sáenz-Cuesta M, Sepúlveda L, Lopetegi I, de Munain AL, Olascoaga J et al (2014) Blood miRNA expression pattern is a possible risk marker for natalizumab-associated progressive multifocal leukoencephalopathy in multiple sclerosis patients. Mult Scler J 20(14):1851–1859

    Article  CAS  Google Scholar 

  39. Guerau-de-Arellano M, Lovett-Racke AE, Racke MK (2010) miRNAs in multiple sclerosis: regulating the regulators. J Neuroimmunol 229(1–2):3–4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yang Q, Pan W, Qian L (2017) Identification of the miRNA-mRNA regulatory network in multiple sclerosis. Neurol Res 39(2):142–151

    Article  CAS  PubMed  Google Scholar 

  41. Fenoglio C et al (2013) Decreased circulating miRNA levels in patients with primary progressive multiple sclerosis. Mult Scler J 19(14):1938–1942

    Article  CAS  Google Scholar 

  42. De Santis G et al (2010) Altered miRNA expression in T regulatory cells in course of multiple sclerosis. J Neuroimmunol 226(1–2):165–171

    Article  PubMed  CAS  Google Scholar 

  43. Zinger A et al (2016) Plasma levels of endothelial and B cell-derived microparticles are restored by fingolimod treatment in multiple sclerosis patients. Mult Scler J 22(14):1883–1887

    Article  CAS  Google Scholar 

  44. Vistbakka J et al (2017) Circulating microRNAs as biomarkers in progressive multiple sclerosis. Mult Scler J 23(3):403–412

    Article  CAS  Google Scholar 

  45. Husakova M (2016) MicroRNAs in the key events of systemic lupus erythematosus pathogenesis. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 160(3):327–342

    Article  PubMed  Google Scholar 

  46. Wu Y et al (2017) Lower serum levels of miR-29c-3p and miR-19b-3p as Biomarkers for Alzheimer’s disease. Tohoku J Exp Med 242(2):129–136

    Article  CAS  PubMed  Google Scholar 

  47. Rossi S et al (2014) Interleukin-1β causes excitotoxic neurodegeneration and multiple sclerosis disease progression by activating the apoptotic protein p53. Mol Neurodegener 9(1):56

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Rawji KS, Yong VW (2013) The benefits and detriments of macrophages/microglia in models of multiple sclerosis. Clin Dev Immunol 2013:948976

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Guarda G et al (2011) Type I interferon inhibits interleukin-1 production and inflammasome activation. Immunity 34(2):213–223

    Article  CAS  PubMed  Google Scholar 

  50. Bhinge A et al (2016) MiR-375 is essential for human spinal motor neuron development and may be involved in motor neuron degeneration. 34(1):124–134

  51. Sievers C et al (2012) Altered microRNA expression in B lymphocytes in multiple sclerosis. Clin Immunol 144(1):70–79

    Article  CAS  PubMed  Google Scholar 

  52. Jernås M, Malmeström C, Axelsson M, Nookaew I, Wadenvik H, Lycke J, Olsson B (2013) MicroRNA regulate immune pathways in T-cells in multiple sclerosis (MS). BMC Immunol 14(32)

  53. Martinelli-Boneschi F et al (2012) MicroRNA and mRNA expression profile screening in multiple sclerosis patients to unravel novel pathogenic steps and identify potential biomarkers. Neurosci Lett 508(1):4–8

    Article  CAS  PubMed  Google Scholar 

  54. Venken K et al (2008) Natural naive CD4+ CD25+ CD127low regulatory T cell (Treg) development and function are disturbed in multiple sclerosis patients: recovery of memory Treg homeostasis during disease progression. J Immunol 180(9):6411–6420

    Article  CAS  PubMed  Google Scholar 

  55. Severin ME et al (2016) MicroRNAs targeting TGFbeta signalling underlie the regulatory T cell defect in multiple sclerosis. Brain 139(Pt 6):1747–1761

    Article  PubMed  PubMed Central  Google Scholar 

  56. Petrocca F, Vecchione A, Croce CM (2008) Emerging role of miR-106b-25/miR-17-92 clusters in the control of transforming growth factor beta signalling. Cancer Res 68(20):8191–8194

    Article  CAS  PubMed  Google Scholar 

  57. Jana M et al (2014) Interleukin-12 (IL-12), but not IL-23, induces the expression of IL-7 in microglia and macrophages: implications for multiple sclerosis. Immunology 141(4):549–563

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Libro R, Bramanti P, Mazzon E (2016) The role of the Wnt canonical signalling in neurodegenerative diseases. Life Sci 158:78–88

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge and thank the patients who were involved in the study. We would like to thank the University of Sydney MS Clinical Trials staff for their assistance with venepuncture, serum preparation and storage and general study facilitation.

Funding

This project was partly funded by Novartis Australia.

Author information

Authors and Affiliations

  1. Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia

    Saeideh Ebrahimkhani & Michael E. Buckland

  2. Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia

    Saeideh Ebrahimkhani, Heidi N. Beadnall, Chenyu Wang, Michael H. Barnett & Michael E. Buckland

  3. Sydney Medical School, University of Sydney, Camperdown, NSW, Australia

    Saeideh Ebrahimkhani, Heidi N. Beadnall, Michael H. Barnett & Michael E. Buckland

  4. Department of Neurology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia

    Heidi N. Beadnall & Michael H. Barnett

  5. Division of Molecular Structural and Computational Biology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia

    Catherine M. Suter

  6. Faculty of Medicine, University of New South Wales (UNSW Sydney), Kensington, NSW, Australia

    Catherine M. Suter

  7. School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), 2106, L2 West, Bioscience South E26, UNSW, Sydney, NSW, 2052, Australia

    Fatemeh Vafaee

Authors
  1. Saeideh Ebrahimkhani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heidi N. Beadnall
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chenyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Catherine M. Suter
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael H. Barnett
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael E. Buckland
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fatemeh Vafaee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MHB, HNB, and MEB conceptualized and designed the study and provided critical review of the manuscript. HNB collected patients’ specimens, clinical and MRI information. SE and CW were involved in technical work for data acquisition; SE performed exosome purification and RNA extraction. FV developed the machine learning and bioinformatics pipelines. FV and SE analysed the data and generated the results. All the authors were involved in data interpretation. FV, SE and CMS drafted the manuscript and prepared the figures. All the authors revised and approved the final version.

Corresponding author

Correspondence to Fatemeh Vafaee.

Ethics declarations

Ethical approval for the study was obtained from the University of Sydney Human Research Ethics Committee (2014/054).

Conflict of Interest

Heidi N Beadnall has received honoraria for presentations and advisory boards and has received educational travel support from Novartis, whom markets Gilenya®. Michael Barnett has received institutional support for research, speaking and/or participation in advisory boards for Biogen, Merck, Novartis, Roche and Sanofi Genzyme. He is a research consultant at Medical Safety Systems and research director for the Sydney Neuroimaging Analysis Centre. Michael E Buckland has received honoraria for presentations from Novartis and Biogen.

Additional information

Publisher’s Note

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

Saeideh Ebrahimkhani and Heidi N. Beadnall are equal authorship contribution.

Michael E. Buckland and Fatemeh Vafaee are equal authorship contribution.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ebrahimkhani, S., Beadnall, H.N., Wang, C. et al. Serum Exosome MicroRNAs Predict Multiple Sclerosis Disease Activity after Fingolimod Treatment. Mol Neurobiol 57, 1245–1258 (2020). https://doi.org/10.1007/s12035-019-01792-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12035-019-01792-6

Keywords

Search

Navigation