Serum Exosome MicroRNAs Predict Multiple Sclerosis Disease Activity after Fingolimod Treatment


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.

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Data Availability

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


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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.


This project was partly funded by Novartis Australia.

Author information




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.

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

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

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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).

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  • Multiple sclerosis
  • Gene expression
  • Exosome microRNAs
  • Fingolimod
  • Biomarker