Anti-MECP2 (aa 1-15) polyclonal antibody

Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of MECP2, especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results.

BackgroundWe present a patient with Rett syndrome (RTT; MECP2) and autosomal-recessive succinic semialdehyde dehydrogenase deficiency (SSADHD; ALDH5A1 (aldehyde dehydrogenase 5a1=SSADH), in whom the current phenotype exhibits features of SSADHD (hypotonia, global developmental delay) and RTT (hand stereotypies, gait anomalies). Methods-Hydroxybutyric acid (GHB) was quantified by UPLC-tandem mass spectrometry, while mutation analysis followed standard methodology of whole-exome sequencing. ResultsThe biochemical hallmark of SSADHD, GHB was increased in the proband's dried bloodspot (DBS; 673 mu M; previous SSADHD DBSs (n=7), range 124-4851 mu M); control range (n=2,831), 0-78 mu M. The proband was compound heterozygous for pathogenic ALDH5A1 mutations (p.(Asn418IlefsTer39); maternal; p.(Gly409Asp); paternal) and a de novo RTT nonsense mutation in MECP2 (p.Arg255*). ConclusionThe major inhibitory neurotransmitter, -aminobutyric acid (GABA), is increased in SSADHD but normal in RTT, although there are likely regional changes in GABA receptor distribution. GABAergic anomalies occur in both disorders, each featuring an autism spectrum phenotype. What effect the SSADHD biochemical anomalies (elevated GABA, GHB) might play in the neurodevelopmental/epileptic phenotype of our patient is currently unknown.