This document describes a study that used a systems biology approach to create a network model of the oligodendrocyte differentiation process. Researchers created an initial curated map and then expanded it into a human-mouse hybrid network by integrating interaction data from public databases. They filtered this network by selecting genes associated with oligodendrocyte differentiation based on expression data analysis and gene ontology terms. Researchers then identified active modules within the network and merged them to define subnetworks of genes and miRNAs involved in oligodendrocyte differentiation.

1. Contact information: Universitätsklinikum Erlangen, Hautklinik, Hartmannstraße 14, 91052 Erlangen www.sysbiomed-erlangen.org
Martina Cantone: martina.cantone@uk-erlangen.de
The Oligodendrocyte Differentiation Network: a Systems
Biology Approach
Martina Cantone1, Martin Eberhardt1, Melanie Küspert2, Simone Reiprich2, Michael Wegner2, Julio Vera1
1Laboratory of Systems Tumor Immunology, Hautklinik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
2Institut für Biochemie, Emil-Fischer-Zentrum, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Biological Background
Oligodendrocytes (OLs) are glial cells part of the Central Nervous System (CNS). Their main
function after maturation is myelinogenesis, which is the synthesis of proteins constituting
myelin sheaths and the process of ensheathment around axons. In the CNS, oligodendroglia
are the only designated cells to generate the protective layer around axons, and possible
damages occurring in adulthood must trigger differentiation of immature oligodendrocytes to
restore neuronal transmission. The OL maturation process is regulated by a complex and large
biochemical network involving several transcription factors, miRNAs and chemical compounds
belonging to different signaling pathways. This study is also of great interest for understanding
biology behind pathologies caused by myelin deficiency, like multiple sclerosis or
leukodystrophies .
Workflow and Aim
Current literature and expert curation were used as the basis to detect key elements involved
in the oligodendrocyte differentiation processes. These elements were collected and
annotated in a map that represents biochemical events taking place at different stages of
differentiation. The map was then automatically extended by integrating interactions
extracted from publicly available databases, and annotated either with human or mouse
genome, so that a wider network was created.
Upon integration of RNA sequencing data in the extended network and after iterative
selection steps, here we present a hybrid human-mouse network that includes a mixture of
confirmed and predicted genes and miRNAs involved in oligodendrocytes differentiation.
Material and Methods
Definition and expansion of core network
 (A) Creation of the Map: expert knowledge annotated and integrated with current
literature; created in CellDesigner (www.celldesigner.com);
 (B) Extension to Hybrid Network: on purpose developed Pythons scripts to extract HGNC
symbols of genes, proteins and microRNAs of the core map and to create a network that
includes all interactions in which they are involved (databases scanning procedure);
procedure repeated once for human and once for mouse annotations; merging of human
and mouse network based on HGNC symbols;
 (C) Selection of Relevant Elements: identification of biological and medical terms
associated to each gene of the map and consequent selection of genes of the extended
network correlated to MeSH and GO terms to reduce the number of interactions;
procedure based on a combination of Python scripts with Cytoscape visualization;
A priori knowledge:
• PubMed
• Expertise curation
CellDesigner:
• Fully annotated map
• Biologically faithful
(A)
Extension:
Introduction of all
known interactions
(B)
CellDesigner curated
map
Databases:
• mirTarBase; TarBase
• miRecords; miRBase
• starBase
• HPRD; HTRIdb
• TransmiR
• STRING
Python scripting:
Automatic integration
of database
knowledge
Cytoscape:
Network visualization
Selection of genes:
Reducing the number of
network nodes
Python scripting:
Listing genes features
and creation of filters;
Cytoscape:
Network visualization
SoftwareWorkflow
Databases:
• Gene Ontology (GO)
• Medical Subject
Headings (MeSH)
(C)
Resultingnetworks
(1)
Integration of high-throughput data and topological analysis
 (D) Analysis of Differentially Expressed Genes and MiRNAs: selection of GEO datasets based
on two criteria: (i) rat genome; (ii) transcriptome and miRNome data. Analyses followed
predefined pipelines according to the data type (Agilent microarray and RNA sequencing);
 (E) Projection of the Data: within Cytoscape , association of each gene to the corresponding
log2(FC) value based on gene official name recognition; filling color of nodes ranges from
blue to red to reflect respectively low or high log2(FC) value;
 (F) Active Modules Identification: two Cytoscape plugins were used to define active
modules according to different methodologies (assignment of scores evaluated with
rigorous statistical method VS differentially expressed analyses results);
 (G) Merging submodules: identification of two main networks: core differentiation including
genes part of all active modules and general differentiation that includes all genes playing a
role during differentiation.
Raw data analysis:
• GSE52564
• GSE21798
R and Rstudio:
Limma package for
statistical analysis
RNA-seq pipeline
SoftwareWorkflowResultingnetwork
Acknowledgements:
This project is funded by the DFG though the SPP 1757 subproject “Molecular Determinants of the Differential Properties of
Embryonic Oligodendrocyte Precursors and Adult NG2 Cells”.
References:
• Budde et al., “Control of Oligodendroglial Cell Number by the miR-17-92 Cluster” Development (Cambridge, England) 137,
no. 13 (July 2010): 2127–32, doi:10.1242/dev.050633;
• Zhang et al., “An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the
Cerebral Cortex” The Journal of Neuroscience 34, no. 36 (September 3, 2014): 11929–47, doi:10.1523/JNEUROSCI.1860-
14.2014.
Projection of
log2(FC) on top
of the network
(D) (E)
Active modules
identification
Merging of
active modules
Cytoscape plugins:
• jActiveModules
• KeyPathwayMiner
Cytoscape:
Intersection and
union of the
resulting networks
(F)
(3)
(G)
(2)
Cytoscape:
Visualization of
differential expression
values on top of the
network
Visualization of an intermediate
step.
View of the network in Cytoscape
format. Network in (3) presents
hybrid filtered network, and in the
detail, human and mouse genes are
distinguished by the use of
uppercase letter to indicate human
genes; nodes are colored according
to the log2(FC) of myelinating OLs vs
mature OLs. The table below
indicates the variation of the size of
networks during the procedure.
Summary
 Creation of a fully annotated map;
 Extension to a human-mouse hybrid and
annotated network;
 Selection of the most relevant set of genes
based on (i) differentially expressed genes and
miRNAs analyses, and on (ii) GO and MeSH
filters based on genes and miRNAs used to
define the initial map;
 Identification of active modules according to
GSE dataset selected;
 Identification of a subnetwork of genes and
miRNAs active through whole differentiation.
Network # Nodes # Edges
Initial
CellDesigner map
(replicates included)
95 98
Mouse / Human
extended (B)
6391 / 19461 32786 /
299346
Mouse / Human
filtered (C)
1328 / 3365 1219 /
8812
Hybrid network
(E)
1584 4340
Active modules
during whole
differentiation (G)
154 282
Modules varying
activation state
during
differentiation (G)
1042 2691
Outlook:
 Experimental validation of sub-networks;
 Integration of additional dataset to validate the robustness of resulting networks;
 Networks refinement according to new data.
Definition of the core network and its expansion. In panel (1) the static map is presented in
CellDesigner format with a detail of the nucleus enlarged, showing the representation of biochemical
reactions; panel (2) reports the non-filtered human-mouse hybrid network. Here, some nodes are
colored according to the expression level of the corresponding gene, before differential expression
analyses.