Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes. (25th November 2020)

• Record Type: Journal Article
• Authors: Ehrhart, Friederike; Coort, Susan L.; Eijssen, Lars; Cirillo, Elisa; Smeets, Eric E.; Bahram Sangani, Nasim; Evelo, Chris T.; Curfs, Leopold M.G.
• Is Part Of: World journal of biological psychiatry. Volume 21:Number 10(2020)
• Page Start: 712
• Page End: 725
• Publication Date: 2020-11-25
• Subjects: Rett syndrome -- rare diseases -- neurological disorder -- bioinformatics -- pathway analysis; Biological psychiatry -- Periodicals; Biological Psychiatry -- Periodicals; 616.89
• DOI: 10.1080/15622975.2019.1593501
• Languages: English
• ISSNs: 1562-2975

Abstract

Abstract: Objectives: Rett syndrome (RTT) is a rare disorder causing severe intellectual and physical disability. The cause is a mutation in the gene coding for the methyl-CpG binding protein 2 (MECP2), a multifunctional regulator protein. Purpose of the study was integration and investigation of multiple gene expression profiles in human cells with impaired MECP2 gene to obtain a robust, data-driven insight in molecular disease mechanisms. Methods: Information about changed gene expression was extracted from five previously published studies, integrated and the resulting differentially expressed genes were analysed using overrepresentation analysis of biological pathways and gene ontology, and network analysis. Results: We identified a set of genes, which are significantly changed not in all but several transcriptomics datasets and were not mentioned in the context of RTT before. We found that these genes are involved in several processes and molecular pathways known to be affected in RTT. Integrating transcription factors we identified a possible link how MECP2 regulates cytoskeleton organisation via MEF2C and CAPG. Conclusions: Integrative analysis of omics data and prior knowledge databases is a powerful approach to identify links between mutation and phenotype especially in rare disease research where little data is available.