Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes

Abstract

(175 words)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 data-driven insight in downstream effects. Information about changed gene expression was extracted from five previously published studies. 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. Using overrepresentation analysis of molecular pathways and gene ontology 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 organization via MEF2C and CAPG. 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. For genes the symbols according to the HGNC nomenclature were used.

Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome

“…The modules mainly contained interactions from transcription / translation and cell cycle related pathways, which were not found with the classical enrichment analysis. These processes were also previously found in transcriptome pathway analysis of Rett syndrome (Bedogni et al, 2014; Ehrhart et al, 2018). Not surprisingly, the subnetworks do not contain metabolic reactions.…”

“…As expected, our pathway and GO analysis revealed a substantial number of immune system related pathways to be affected in Rett syndrome frontal and temporal cortex tissue samples. Inflammatory processes have been identified previously in Rett syndrome patients, mouse models and in vitro systems, and are suspected to contribute to the development of Rett syndrome (De Felice et al, 2016; Ehrhart et al, 2018). Figure 2 shows many of affected pathways in both frontal and temporal cortex, with similar results found by GO analysis.…”

“…MECP2 was generally regarded as a repressor, however its role as genetic activator has also been confirmed (Chahrour et al, 2008). In previous studies, a loss of function in MECP2 due to a mutation has been found to influence a variety of pathways and biological processes, including pathways related to not only neuron development and function, but also to the immune system, transcription, and translation related processes (which were identified mainly by transcriptome analysis, Colantuoni et al, 2001; Bedogni et al, 2014; Ehrhart et al, 2018; Shovlin and Tropea, 2018). The affected pathways identified with our study closely match the results previously found by Ehrhart et al (2018), in which human brain tissue data of Rett syndrome patients (published by Deng et al, 2007) was analyzed.…”

“…Shovlin and Tropea (2018) recently reviewed the available transcriptomics studies on Rett syndrome and came to the conclusion that the most researched impact of MECP2 dysfunction lies with dendritic connectivity and synapse maturation, mitochondrial dysfunction, and glial cell activity. Recent pathway analysis results of single and integrated studies identified changes in intracellular signaling, including EIF2 (eukaryotic translation initiation) signaling, cytoskeleton, and cell metabolism including mitochondrial function (Bedogni et al, 2014; Ehrhart et al, 2018).…”

Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions

“…The severity of the disorder is related to the specific mutation found in the individual patient [14]. Ehrhart et al have already demonstrated the power of integrating different databases to retrieve links between genetic variants and phenotypes in the cases of a rare disease for which a limited amount of data is available in a single place [15]. Being able to look at alternative targets that are a part of the sequence of developments that lead to disorders such as Rett may end up helping us to expand the knowledge about alternative causes and treatment opportunities.…”