ECCB poster.pdf (858.57 kB)
Poster ECCB 2016 Pathway analysis of Rett syndrome omics data
This is a poster presented at the ECCB 2016 in The Hague.
Although being a rare disease, Rett syndrome (RTT) is one of the most common neurological disorders in females. The typical symptoms are regression and loss of acquired motor and communication skills after the first 6-18 months of normal pre- and postnatal development. RTT females are generally suffering severe intellectual disability, learning disability, and motor impairments. Cause for RTT is a mutation in one single gene, the methyl-CpG-binding protein 2 (MECP2). MECP2 is a central signaling gene which acts as global and gene specific transcription regulator, shapes chromatin, influences alternative splicing, and epigenetic imprinting [1]. The molecular pathways leading from the gene to the different symptoms are not yet fully understood. Using systems biology approaches to reveal the mechanism of rare diseases is highly interesting because it draws attention on the whole system instead of single endpoints. Pathway analysis and visualisation of the results allows better understanding through combination of experimental data with existing knowledge. For this purpose we created a RTT pathway for WikiPathways which can be used for analysis. In this study we demonstrate the use of pathway approaches to analyse and visualise molecular data. This is a multiomics approach integrating transcriptomics and metabolomics data.
Although being a rare disease, Rett syndrome (RTT) is one of the most common neurological disorders in females. The typical symptoms are regression and loss of acquired motor and communication skills after the first 6-18 months of normal pre- and postnatal development. RTT females are generally suffering severe intellectual disability, learning disability, and motor impairments. Cause for RTT is a mutation in one single gene, the methyl-CpG-binding protein 2 (MECP2). MECP2 is a central signaling gene which acts as global and gene specific transcription regulator, shapes chromatin, influences alternative splicing, and epigenetic imprinting [1]. The molecular pathways leading from the gene to the different symptoms are not yet fully understood. Using systems biology approaches to reveal the mechanism of rare diseases is highly interesting because it draws attention on the whole system instead of single endpoints. Pathway analysis and visualisation of the results allows better understanding through combination of experimental data with existing knowledge. For this purpose we created a RTT pathway for WikiPathways which can be used for analysis. In this study we demonstrate the use of pathway approaches to analyse and visualise molecular data. This is a multiomics approach integrating transcriptomics and metabolomics data.
Funding
This study has been funded by The Dutch Rett Syndrome Foundation - Stichting Terre.
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Categories
- Statistical and quantitative genetics
- Epigenetics (incl. genome methylation and epigenomics)
- Proteomics and intermolecular interactions (excl. medical proteomics)
- Gene expression (incl. microarray and other genome-wide approaches)
- Animal physiology - systems
- Cell metabolism
- Signal transduction
- Receptors and membrane biology
- Behavioural neuroscience
- Animal cell and molecular biology
- Plant cell and molecular biology
- Bioinformatics and computational biology not elsewhere classified
- Biochemistry and cell biology not elsewhere classified
- Systems biology
- Neurosciences not elsewhere classified
- Cell neurochemistry
Keywords
Rett syndromerare diseasesdata integrationsystems biologyMECP2molecular datapathway analysisdata infrastructureELIXIRQuantitative Genetics (incl. Disease and Trait Mapping Genetics)Epigenetics (incl. Genome Methylation and Epigenomics)Proteomics and Intermolecular Interactions (excl. Medical Proteomics)Gene Expression (incl. Microarray and other genome-wide approaches)Animal Physiology - SystemsCell MetabolismSignal TransductionReceptors and Membrane BiologyBehavioral NeuroscienceAnimal Cell and Molecular BiologyMolecular BiologyBioinformaticsComputational BiologyCell BiologySystems BiologyNeuroscienceCell Neurochemistry
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