New insights in Rett syndrome using pathway analysis for transcriptomics data

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

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.

Early Signs of Molecular Defects in iPSC-Derived Neural Stems Cells from Patients with Familial Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, classically associated with extensive loss of dopaminergic neurons of the substantia nigra pars compacta. The hallmark of the disease is the accumulation of pathogenic conformations of the presynaptic protein, α-synuclein (αSyn), and the formation of intraneuronal protein aggregate inclusions. Neurodegeneration of dopamine neurons leads to a prominent dopaminergic deficiency in the basal ganglia, responsible for motor disturbances. However, it is now recognized that the disease involves more widespread neuronal dysfunction, leading to early and late non-motor symptoms. The development of in vitro systems based on the differentiation of human-induced pluripotent stem cells provides us the unique opportunity to monitor alterations at the cellular and molecular level throughout the differentiation procedure and identify perturbations that occur early, even at the neuronal precursor stage. Here we aim to identify whether p.A53T-αSyn induced disturbances at the molecular level are already present in neural precursors. Towards this, we present data from transcriptomics analysis of control and p.A53T-αSyn NPCs showing altered expression in transcripts involved in axon guidance, adhesion, synaptogenesis, ion transport, and metabolism. The comparative analysis with the transcriptomics profile of p.A53T-αSyn neurons shows both distinct and overlapping pathways leading to neurodegeneration while meta-analysis with transcriptomics data from both neurodegenerative and neurodevelopmental disorders reveals that p.A53T-pathology has a significant overlap with the latter category. This is the first study showing that molecular dysregulation initiates early at the p.A53T-αSyn NPC level, suggesting that synucleinopathies may have a neurodevelopmental component.

Megaconial congenital muscular dystrophy secondary to novel CHKB mutations resemble atypical Rett syndrome

Megaconial congenital muscular dystrophy (CMD)(OMIM #602541), related to CHKB mutation, is a rare autosomal recessive disorder. To date, only 35 confirmed patients are recorded. We present a detailed description of the clinical, histopathological, imaging, and genetic findings of five children from four Indian families. The children had moderate-to-severe autistic behavior, hand stereotypies, and global developmental delay mimicking atypical Rett syndrome. In addition, generalized hypotonia was a common initial finding. The progression of muscle weakness was variable, with two patients having a milder phenotype and three having a severe form. Interestingly, the majority did not attain sphincter control. Only patient 1 had classical ichthyotic skin changes. Muscle biopsy in two patients showed a myopathic pattern with characteristic peripherally placed enlarged mitochondria on modified Gomori trichrome stain and electron microscopy. Genetic analysis in these patients identified three novel null mutations in CHKB [c.1027dupA (p.Ser343LysfsTer86);c.224 + 1G > T (5' splice site); c.1123C > T (p.Gln375Ter)] and one reported missense mutation, c.581G > A (p.Arg194Gln), all in the homozygous state. Megaconial CMD, although rare, forms an important group with a complex phenotypic presentation and accounted for 5.5% of our genetically confirmed CMD patients. Atypical Rett syndrome-like presentation may be a clue towards CHKB-related disorder.

A proteomics approach to further highlight the altered inflammatory condition in Rett syndrome

Rett syndrome (RTT) is a progressive neurodevelopmental disorder caused by mutations in the X-linked MECP2 gene. RTT patients show multisystem disturbances associated with perturbed redox homeostasis and inflammation, which appear as possible key factors in RTT pathogenesis. In this study, using primary dermal fibroblasts from control and RTT subjects, we performed a proteomic analysis that, together with data mining approaches, allowed us to carry out a comprehensive characterization of RTT cellular proteome. Functional and pathway enrichment analyses showed that differentially expressed proteins in RTT were mainly enriched in biological processes related to immune/inflammatory responses. Overall, by using proteomic data mining as supportive approach, our results provide a detailed insight into the molecular pathways involved in RTT immune dysfunction that, causing tissue and organ damage, can increase the vulnerability of affected patients to unknown endogenous factors or infections.

Genome-wide transcriptomic and proteomic studies of Rett syndrome mouse models identify common signaling pathways and cellular functions as potential therapeutic targets

The discovery that Rett syndrome is caused by mutations in the MECP2 gene has provided a major breakthrough in our understanding of the disorder. However, despite this, there is still limited understanding of the underlying pathophysiology of the disorder hampering the development of curative treatments. Over the years, a number of animal models have been developed contributing to our knowledge of the role of MECP2 in development and improving our understanding of how subtle expression levels affect brain morphology and function. Transcriptomic and proteomic studies of animal models are useful in identifying perturbations in functional pathways and providing avenues for novel areas of research into disease. This review focuses on published transcriptomic and proteomic studies of mouse models of Rett syndrome with the aim of providing a summary of all the studies, the reported dysregulated genes and functional pathways that are found to be perturbed. The 36 articles identified highlighted a number of dysfunctional pathways as well as perturbed biological networks and cellular functions including synaptic dysfunction and neuronal transmission, inflammation, and mitochondrial dysfunction. These data reveal biological insights that contribute to the disease process which may be targeted to investigate curative treatments.

Transcriptomic Studies in Mouse Models of Rett Syndrome: A Review

Rett Syndrome (RTT) is a neurological disorder mainly associated with mutations in the X-linked gene coding for the methyl-CpG binding protein 2 (MECP2). To assist in studying MECP2's function, researchers have generated Mecp2 mouse mutants showing that MECP2's product (MeCP2) mostly functions as a transcriptional regulator. During the last two decades, these models have been used to determine the genes that are regulated by MeCP2, slowly dissecting the etiological mechanisms underlying RTT. In the present review, we describe the findings of these transcriptomic studies, and highlight differences between them, and discuss how studies on these genetic models can sharpen our understanding of the human disorder. We conclude that - while there's large variability regarding the number of differentially expressed genes identified - there are overlapping features that inform on the biology of RTT.

CyTargetLinker app update: A flexible solution for network extension in Cytoscape

Here, we present an update of the open-source CyTargetLinker app for Cytoscape ( http://apps.cytoscape.org/apps/cytargetlinker) that introduces new automation features. CyTargetLinker provides a simple interface to extend networks with links to relevant data and/or knowledge extracted from so-called linksets. The linksets are provided on the CyTargetLinker website ( https://cytargetlinker.github.io/) or can be custom-made for specific use cases. The new automation feature enables users to programmatically execute the app's functionality in Cytoscape (command line tool) and with external tools (e.g. R, Jupyter, Python, etc). This allows users to share their analysis workflows and therefore increase repeatability and reproducibility. Three use cases demonstrate automated workflows, combinations with other Cytoscape apps and core Cytoscape functionality. We first extend a protein-protein interaction network created with the stringApp, with compound-target interactions and disease-gene annotations. In the second use case, we created a workflow to load differentially expressed genes from an experimental dataset and extend it with gene-pathway associations. Lastly, we chose an example outside the biological domain and used CyTargetLinker to create an author-article-journal network for the five authors of this manuscript using a two-step extension mechanism. With 400 downloads per month in the last year and nearly 20,000 downloads in total, CyTargetLinker shows the adoption and relevance of the app in the field of network biology. In August 2019, the original publication was cited in 83 articles demonstrating the applicability in biomedical research.

CyTargetLinker app update: A flexible solution for network extension in Cytoscape

Here, we present an update of the open-source CyTargetLinker app for Cytoscape ( http://apps.cytoscape.org/apps/cytargetlinker) that introduces new automation features. CyTargetLinker provides a simple interface to extend networks with links to relevant data and/or knowledge extracted from so-called linksets. The linksets are provided on the CyTargetLinker website ( https://cytargetlinker.github.io/) or can be custom-made for specific use cases. The new automation feature enables users to programmatically execute the app’s functionality in Cytoscape (command line tool) and with external tools (e.g. R, Jupyter, Python, etc). This allows users to share their analysis workflows and therefore increase repeatability and reproducibility. Three use cases demonstrate automated workflows, combinations with other Cytoscape apps and core Cytoscape functionality. We first extend a protein-protein interaction network created with the stringApp, with compound-target interactions and disease-gene annotations. In the second use case, we created a workflow to load differentially expressed genes from an experimental dataset and extend it with gene-pathway associations. Lastly, we chose an example outside the biological domain and used CyTargetLinker to create an author-article-journal network for the five authors of this manuscript using a two-step extension mechanism.

With 400 downloads per month in the last year and nearly 20,000 downloads in total, CyTargetLinker shows the adoption and relevance of the app in the field of network biology. In August 2019, the original publication was cited in 83 articles demonstrating the applicability in biomedical research.