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The transcription factors Sox10 and Myrf define an essential regulatory network module in differentiating oligodendrocytes.

ABSTRACT: Myelin is essential for rapid saltatory conduction and is produced by Schwann cells in the peripheral nervous system and oligodendrocytes in the central nervous system. In both cell types the transcription factor Sox10 is an essential component of the myelin-specific regulatory network. Here we identify Myrf as an oligodendrocyte-specific target of Sox10 and map a Sox10 responsive enhancer to an evolutionarily conserved element in intron 1 of the Myrf gene. Once induced, Myrf cooperates with Sox10 to implement the myelination program as evident from the physical interaction between both proteins and the synergistic activation of several myelin-specific genes. This is strongly reminiscent of the situation in Schwann cells where Sox10 first induces and then cooperates with Krox20 during myelination. Our analyses indicate that the regulatory network for myelination in oligodendrocytes is organized along similar general principles as the one in Schwann cells, but is differentially implemented.

SUBMITTER: Hornig J

PROVIDER: S-EPMC3814293 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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The transcription factors Sox10 and Myrf define an essential regulatory network module in differentiating oligodendrocytes.

Hornig Julia J Fröb Franziska F Vogl Michael R MR Hermans-Borgmeyer Irm I Tamm Ernst R ER Wegner Michael M

PLoS genetics 20131031 10

Myelin is essential for rapid saltatory conduction and is produced by Schwann cells in the peripheral nervous system and oligodendrocytes in the central nervous system. In both cell types the transcription factor Sox10 is an essential component of the myelin-specific regulatory network. Here we identify Myrf as an oligodendrocyte-specific target of Sox10 and map a Sox10 responsive enhancer to an evolutionarily conserved element in intron 1 of the Myrf gene. Once induced, Myrf cooperates with Sox ...[more]

PMID: 24204311

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Project description:BackgroundCell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.ResultsWe present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmaniConclusionRMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.

Dataset Information

The transcription factors Sox10 and Myrf define an essential regulatory network module in differentiating oligodendrocytes.

Publications

The transcription factors Sox10 and Myrf define an essential regulatory network module in differentiating oligodendrocytes.

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