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Published in: Orphanet Journal of Rare Diseases 1/2016

Open Access 01-12-2016 | Review

Rett syndrome – biological pathways leading from MECP2 to disorder phenotypes

Authors: Friederike Ehrhart, Susan L. M. Coort, Elisa Cirillo, Eric Smeets, Chris T. Evelo, Leopold M. G. Curfs

Published in: Orphanet Journal of Rare Diseases | Issue 1/2016

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Abstract

Rett syndrome (RTT) is a rare disease but still one of the most abundant causes for intellectual disability in females. Typical symptoms are onset at month 6–18 after normal pre- and postnatal development, loss of acquired skills and severe intellectual disability. The type and severity of symptoms are individually highly different. A single mutation in one gene, coding for methyl-CpG-binding protein 2 (MECP2), is responsible for the disease. The most important action of MECP2 is regulating epigenetic imprinting and chromatin condensation, but MECP2 influences many different biological pathways on multiple levels although the molecular pathways from gene to phenotype are currently not fully understood. In this review the known changes in metabolite levels, gene expression and biological pathways in RTT are summarized, discussed how they are leading to some characteristic RTT phenotypes and therefore the gaps of knowledge are identified. Namely, which phenotypes have currently no mechanistic explanation leading back to MECP2 related pathways? As a result of this review the visualization of the biologic pathways showing MECP2 up- and downstream regulation was developed and published on WikiPathways which will serve as template for future omics data driven research. This pathway driven approach may serve as a use case for other rare diseases, too.
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Metadata
Title
Rett syndrome – biological pathways leading from MECP2 to disorder phenotypes
Authors
Friederike Ehrhart
Susan L. M. Coort
Elisa Cirillo
Eric Smeets
Chris T. Evelo
Leopold M. G. Curfs
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Orphanet Journal of Rare Diseases / Issue 1/2016
Electronic ISSN: 1750-1172
DOI
https://doi.org/10.1186/s13023-016-0545-5

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