Molecular Neurobiology

, Volume 45, Issue 3, pp 520–535 | Cite as

Integration of MicroRNA Databases to Study MicroRNAs Associated with Multiple Sclerosis

  • Charlotte Angerstein
  • Michael HeckerEmail author
  • Brigitte Katrin Paap
  • Dirk Koczan
  • Madhan Thamilarasan
  • Hans-Jürgen Thiesen
  • Uwe Klaus Zettl


MicroRNAs (miRNAs) are small non-coding RNAs which regulate many genes post-transcriptionally. In various contexts of medical science, miRNAs gained increasing attention over the last few years. Analyzing the functions, interactions and cellular effects of miRNAs is a very complex and challenging task. Many miRNA databases with diverse data contents have been developed. Here, we demonstrate how to integrate their information in a reasonable way on a set of miRNAs that were found to be dysregulated in the blood of patients with multiple sclerosis (MS). Using the miR2Disease database, we retrieved 16 miRNAs associated with MS according to four different studies. We studied the predicted and experimentally validated target genes of these miRNAs, their expression profiles in different blood cell types and brain tissues, the pathways and biological processes affected by these miRNAs as well as their regulation by transcription factors. Only miRNA–mRNA interactions that were predicted by at least seven different prediction algorithms were considered. This resulted in a network of 1,498 target genes. In this network, the MS-associated miRNAs hsa-miR-20a-5p and hsa-miR-20b-5p occurred as central hubs regulating about 500 genes each. Strikingly, many of the putative target genes play a role in T cell activation and signaling, and many have transcription factor activity. The latter suggests that miRNAs often act as regulators of regulators with many secondary effects on gene expression. Our present work provides a guideline on how information of different databases can be integrated in the analysis of miRNAs. Future investigations of miRNAs shall help to better understand the mechanisms underlying different diseases and their treatments.


Multiple sclerosis MicroRNA Databases Gene regulatory networks 



Clinically isolated syndrome


Central nervous system


Experimental autoimmune encephalomyelitis


Gene Ontology




Multiple sclerosis


Next generation sequencing


Peripheral blood mononuclear cells






Position weight matrix


RNA-induced silencing complex


Relapsing-remitting multiple sclerosis


Single nucleotide polymorphism


Transcription factor


Transcription factor binding site

Treg cell

T regulatory cell



This work was co-funded by the United Europeans for the development of PHArmacogenomics in Multiple Sclerosis consortium (UEPHA*MS), which had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We thank Ulf Schmitz for helpful discussions.

Conflicts of interest

The authors declare that they have no conflict of interest.

Supplementary material

12035_2012_8270_MOESM1_ESM.cys (104 kb)
Online Resource 1 Cytoscape session file of the miRNA interaction network. The Cytoscape network contains computationally predicted and experimentally determined interactions between MS-associated miRNAs and their putative mRNA targets. These interactions were obtained from the databases miRWalk and miRTarBase, respectively. Moreover, potential regulatory influences of transcription factors on the expression of the miRNAs were included as well. This information was taken from the miRGen 2.0 database. A visualization of the network is shown in Fig. 3. (CYS 103 kb)
12035_2012_8270_MOESM2_ESM.xls (304 kb)
Online Resource 2 Table showing the grouping of similar TFBS motifs. The miRGen database provides predictions of TFBS in the promoter regions of miRNA-coding genes based on PWMs from the Transfac database. We used the web tool STAMP [76] to group similar Transfac PWMs. The Table shows, which Transfac PWMs were found to be related to each other. Predictions of similar TFBS were pooled. In total, 39 different DNA-binding patterns were distinguished and visualized in the network as TF nodes (Fig. 3, Online Resource 1). PWM = position weight matrix, TF = transcription factor, TFBS = TF binding site (XLS 304 kb)


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Charlotte Angerstein
    • 1
  • Michael Hecker
    • 1
    • 2
    Email author
  • Brigitte Katrin Paap
    • 1
    • 2
  • Dirk Koczan
    • 2
  • Madhan Thamilarasan
    • 1
    • 2
  • Hans-Jürgen Thiesen
    • 2
  • Uwe Klaus Zettl
    • 1
  1. 1.Department of Neurology, Division of NeuroimmunologyUniversity of RostockRostockGermany
  2. 2.Institute of ImmunologyUniversity of RostockRostockGermany

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