Identification of molecular signatures and pathways involved in Rett syndrome using a multi-omics approach

Abstract

Abstract Background Rett syndrome (RTT) is a neurodevelopmental disorder mainly caused by mutations in the methyl-CpG-binding protein 2 gene (MECP2). MeCP2 is a multifunctional protein involved in many cellular processes, but the mechanisms by which its dysfunction causes disease are not fully understood. The duplication of MECP2 is the cause of a different disorder, MECP2 duplication syndrome (MDS), indicating that its dosage must be tightly regulated for proper cellular function. Moreover, there are patients with a remarkable phenotypic overlap with RTT and mutations in genes other than MECP2 (RTT-like), suggesting they could be involved in similar cellular functions. The purpose of this study was to characterize the molecular alterations in patients with RTT in order to identify potential biomarkers or therapeutic targets for this disorder. Methods We used a combination of transcriptomics (RNAseq) and proteomics (TMT-mass spectrometry) to characterize the expression patterns in fibroblast cell lines from 22 patients with RTT and detected mutation in MECP2, 15 patients with MDS, 12 patients with RTT-like phenotypes and 13 healthy controls. Transcriptomics and proteomics data were used to identify differentially expressed genes both at RNA and protein levels, which were further inspected via enrichment and upstream regulator analyses and compared to find shared features in patients with RTT. Results We identified molecular alterations in cellular functions and pathways that may contribute to the disease phenotype in patients with RTT,such as deregulated cytoskeletal components, vesicular transport elements, ribosomal subunits and mRNA processsing machinery. We also compared RTT expression profiles with those of MDS seeking changes in opposite directions that could lead to the identification of MeCP2 direct targets. Some of the deregulated transcripts and proteins were consistently affected in patients with RTT-like phenotypes, revealing potentially relevant molecular processes in patients with overlapping traits and different genetic aetiology. Conclusions The integration of data in a multi-omic analysis has helped to interpret the molecular consequences of MECP2 dysfunction, contributing to the characterisation of the molecular landscape in patients with RTT. The comparison with MDS provides knowledge of MeCP2 direct targets, whilst the correlation with RTT-like phenotypes highlights processes potentially contributing to the pathomechanism leading these disorders.