Meta-Analysis Identifies BDNF and Novel Common Genes Differently Altered in Cross-Species Models of Rett Syndrome

Abstract

Rett syndrome (RTT) is a rare disorder and one of the most abundant causes of intellectual disabilities in females. Single mutations in the gene coding for methyl-CpG-binding protein 2 (MeCP2) are responsible for the disorder. MeCP2 regulates gene expression as a transcriptional regulator as well as through epigenetic imprinting and chromatin condensation. Consequently, numerous biological pathways on multiple levels are influenced. However, the exact molecular pathways from genotype to phenotype are currently not fully elucidated. Treatment of RTT is purely symptomatic as no curative options for RTT have yet to reach the clinic. The paucity of this is mainly due to an incomplete understanding of the underlying pathophysiology of the disorder with no clinically useful common disease drivers, biomarkers, or therapeutic targets being identified. With the premise of identifying universal and robust disease drivers and therapeutic targets, here, we interrogated a range of RTT transcriptomic studies spanning different species, models, and MECP2 mutations. A meta-analysis using RNA sequencing data from brains of RTT mouse models, human post-mortem brain tissue, and patient-derived induced pluripotent stem cell (iPSC) neurons was performed using weighted gene correlation network analysis (WGCNA). This study identified a module of genes common to all datasets with the following ten hub genes driving the expression: ATRX, ADCY7, ADCY9, SOD1, CACNA1A, PLCG1, CCT5, RPS9, BDNF, and MECP2. Here, we discuss the potential benefits of these genes as therapeutic targets.

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

“…ConclusionsSeveral groups have studied RTT transcriptome using different assays and RTT models in the last 21 years. As a result, more than 60 articles have been published[65,99,107,[145][146][147][148][149][150][151][152][153][154][155][156][157][158][159][160][161][162][163]. Despite the differences among the studies, 75% of the signi cant DEGs identi ed in this study have already been reported in prior studies [data not shown].…”

Neuroprotective and Disease-Modifying Effects of the Triazinetrione ACD856, a Positive Allosteric Modulator of Trk-Receptors for the Treatment of Cognitive Dysfunction in Alzheimer’s Disease

“…Apart from its role in AD, BDNF has also been shown to be decreased in neuropsychiatric disorders such as depression [ 25 ] and post-traumatic stress disorder (PTSD) [ 26 ]. A role for BDNF in Rett syndrome has also been described, suggesting that dysregulation of the BDNF gene may affect the severity of this pathology [ 27 , 28 , 29 ].…”

Comprehensive Transcriptomic Investigation of Rett Syndrome Reveals Increasing Complexity Trends from Induced Pluripotent Stem Cells to Neurons with Implications for Enriched Pathways

“…However, the need to use MECP2 heterozygous female mice and the inconsistency of symptoms in mice compared to humans due to differences in gene expression limit the usability of these models . For this reason, humanized in vitro models using neural/glial cells or brain organoids originating from patient-derived induced pluripotent stem cells (iPSCs), have gained importance for obtaining transcriptomic and proteomic data for NDDs with state-of-the-art technology in the recent past. , As human transcriptome data are difficult to find for RTT, as a rare disease, within this scope, the number of studies in which more than one cell group or brain tissue is analyzed and compared with the integration of transcriptomic data is gaining importance. − The scope, content, and intensity of data should be sufficient and specific to human tissues in order to have better insights into the genetic level of the disease in humans. With the latest developments in next-generation sequencing (NGS) and bioinformatic tools, a large amount of data has been obtained, which has accelerated the understanding of disease mechanisms and elicited new treatment targets by analyzing the data.…”