Expanding the MECP2 network using comparative genomics reveals potential therapeutic targets for Rett syndrome

Role of histone deacetylase inhibitors in non-neoplastic diseases

Background

Epigenetic dysregulation has been implicated in the development and progression of a variety of human diseases, but epigenetic changes are reversible, and epigenetic enzymes and regulatory proteins can be targeted using small molecules. Histone deacetylase inhibitors (HDACis), as a class of epigenetic drugs, are widely used to treat various cancers and other diseases involving abnormal gene expression.

Results

Specially, HDACis have emerged as a promising strategy to enhance the therapeutic effect of non-neoplastic conditions, including neurological disorders, cardiovascular diseases, renal diseases, autoimmune diseases, inflammatory diseases, infectious diseases and rare diseases, along with their related mechanisms. However, their clinical efficacy has been limited by drug resistance and toxicity.

Conclusions

To date, most clinical trials of HDAC inhibitors have been related to the treatment of cancer rather than the treatment of non-cancer diseases, for which experimental studies are gradually underway. Discussions regarding non-neoplastic diseases often concentrate on specific disease types. Therefore, this review highlights the development of HDACis and their potential therapeutic applications in non-neoplastic diseases, either as monotherapy or in combination with other drugs or therapies.

Biochemical and molecular determinants of the subclinical inflammatory mechanisms in Rett syndrome

To date, Rett syndrome (RTT), a genetic disorder mainly caused by mutations in the X-linked MECP2 gene, is increasingly considered a broad-spectrum pathology, instead of just a neurodevelopmental disease, due to the multitude of peripheral co-morbidities and the compromised metabolic pathways, affecting the patients. The altered molecular processes include an impaired mitochondrial function, a perturbed redox homeostasis, a chronic subclinical inflammation and an improper cholesterol metabolism. The persistent subclinical inflammatory condition was first defined ten years ago, as a previously unrecognized feature of RTT, playing a role in the pathology progress and modulation of phenotypical severity. In light of this, the present work aims at reviewing the current knowledge on the chronic inflammatory status and the altered immune/inflammatory functions in RTT, as well as investigating the emerging mechanisms underlying this condition with a special focus on the latest findings about inflammasome system, autoimmunity responses and intestinal micro- and mycobiota. On these bases, although further research is needed, future therapeutic strategies able to re-establish an adequate immune/inflammatory response could represent potential approaches for RTT patients.

Using multi-scale genomics to associate poorly annotated genes with rare diseases

BACKGROUND

Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes.

METHODS

EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated on solved exomes and simulated genomes, compared with existing methods, and applied to 6260 knockout genes with mouse phenotypes lacking human associations. Additionally, EvORanker was made accessible as a user-friendly web tool.

RESULTS

In the analyzed exomic cohort, EvORanker accurately identified the "true" disease gene as the top candidate in 69% of cases and within the top 5 candidates in 95% of cases, consistent with results from the simulated dataset. Notably, EvORanker outperformed existing methods, particularly for poorly annotated genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker linked 41% of these genes to observed human disease phenotypes. Furthermore, in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3 as disease candidates for previously uncharacterized genetic syndromes.

CONCLUSIONS

We highlight clade-based phylogenetic profiling as a powerful systematic approach for prioritizing potential disease genes. Our study showcases the efficacy of EvORanker in associating poorly annotated genes to disease phenotypes observed in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/ .

Propofol Inhibits Glioma Stem Cell Growth and Migration and Their Interaction with Microglia via BDNF-AS and Extracellular Vesicles

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. GBM contains a small subpopulation of glioma stem cells (GSCs) that are implicated in treatment resistance, tumor infiltration, and recurrence, and are thereby considered important therapeutic targets. Recent clinical studies have suggested that the choice of general anesthetic (GA), particularly propofol, during tumor resection, affects subsequent tumor response to treatments and patient prognosis. In this study, we investigated the molecular mechanisms underlying propofol's anti-tumor effects on GSCs and their interaction with microglia cells. Propofol exerted a dose-dependent inhibitory effect on the self-renewal, expression of mesenchymal markers, and migration of GSCs and sensitized them to both temozolomide (TMZ) and radiation. At higher concentrations, propofol induced a large degree of cell death, as demonstrated using microfluid chip technology. Propofol increased the expression of the lncRNA BDNF-AS, which acts as a tumor suppressor in GBM, and silencing of this lncRNA partially abrogated propofol's effects. Propofol also inhibited the pro-tumorigenic GSC-microglia crosstalk via extracellular vesicles (EVs) and delivery of BDNF-AS. In conclusion, propofol exerted anti-tumor effects on GSCs, sensitized these cells to radiation and TMZ, and inhibited their pro-tumorigenic interactions with microglia via transfer of BDNF-AS by EVs.

Methyl-CpG Binding Protein 2 as a Potential Diagnostic and Prognostic Marker Facilitates Glioma Progression Through Activation of Wnt/β-Catenin Pathway

BACKGROUND

Glioma is the primary malignant tumor in the central nervous system and has high malignancy, mortality, and recurrence rates. Because of its heterogeneity and drug resistance, the blood-brain barrier, and other factors, the treatment of glioma has mainly been surgical resection combined with traditional radiotherapy and chemotherapy. However, the therapeutic effect has not been satisfactory. Methyl-CpG binding protein 2 (MeCP2) is an epigenetic regulator that has been reported to regulate the initiation and progression of glioma. However, the underlying mechanism in glioma has remained unclear.

METHODS

The gene expression of MeCp2, miR-138-5p, the epithelial-mesenchymal transition, the apoptosis-related gene, and the Wnt/β-Catenin pathway-related gene and proliferation were detected by reverse transcription-quantitative polymerase chain reaction or Western blot. The cell proliferation and apoptosis of the glioma cell was assessed using the CCK-8 assay and flow cytometry assay. The relationship between miR-138-5p and MeCp2 was measured using the dual luciferase reporter assay. The effect of MeCp2 in U87 cells was examined in a xenograft tumorigenesis model in vivo.

RESULTS

In our study, we found that MeCP2 was upregulated in glioma tissues and cell lines and that MeCP2 knockdown repressed cell proliferation and epithelial-mesenchymal transition but boosted cell apoptosis in glioma. Furthermore, MeCP2 knockdown attenuated in vivo glioma growth in a mice model. Mechanistically, miR-138-5p hindered the expression of MeCP2 by target MeCP2 and then inactivated the Wnt/β-catenin signaling pathway. In addition, subsequent rescue assays disclosed that miR-138-5p repressed the glioma malignant phenotype and MeCP2 overexpression reversed the inhibitory effect of miR-138-5p upregulation. Consistently, overexpression of MeCP2 elevated glioma development. However, inhibition of the Wnt/β-catenin signaling pathway with XAV-939 rescued the facilitation effect by overexpressing miR-138-5p.

CONCLUSIONS

Our results have revealed that miR-138-5p/MeCP2/Wnt/β-catenin signaling might be a new target axis for glioma treatment strategies.

State-of-the-art therapies for Rett syndrome

Rett syndrome (RTT) is an X-linked neurogenetic disorder caused by mutations of the MECP2 (methyl-CpG-binding protein 2) gene. Over two decades of work established MeCP2 as a protein with pivotal roles in the regulation of the epigenome, neuronal physiology, synaptic maintenance, and behaviour. Given the genetic aetiology of RTT and the proof of concept of its reversal in a mouse model, considerable efforts have been made to design therapeutic approaches to re-express MeCP2. By being at the forefront of the development of innovative gene therapies, research on RTT is of paramount importance for the treatment of monogenic neurological diseases. Here we discuss the recent advances and challenges of promising genetic strategies for the treatment of RTT including gene replacement therapies, gene/RNA editing strategies, and reactivation of the silenced X chromosome. WHAT THIS PAPER ADDS: Recent advances shed light on the promises of gene replacement therapy with new vectors designed to control the levels of MeCP2 expression. New developments in DNA/RNA editing approaches or reactivation of the silenced X chromosome open the possibility to re-express the native MeCP2 locus at endogenous levels. Current strategies still face limitations in transduction efficiency and future work is needed to improve brain delivery.

KW-2449 and VPA exert therapeutic effects on human neurons and cerebral organoids derived from MECP2-null hESCs

BACKGROUND

Rett syndrome (RTT), mainly caused by mutations in methyl-CpG binding protein 2 (MECP2), is one of the most prevalent neurodevelopmental disorders in girls. However, the underlying mechanism of MECP2 remains largely unknown and currently there is no effective treatment available for RTT.

METHODS

We generated MECP2-KO human embryonic stem cells (hESCs), and differentiated them into neurons and cerebral organoids to investigate phenotypes of MECP2 loss-of-function, potential therapeutic agents, and the underlying mechanism by transcriptome sequencing.

RESULTS

We found that MECP2 deletion caused reduced number of hESCs-derived neurons and simplified dendritic morphology. Moreover, MECP2-KO cortical organoids exhibited fewer neural progenitor cells and neurons at day 60. Electrophysiological recordings showed that MECP2 deletion altered synaptic activity in organoids. Transcriptome analysis of organoids identified many genes in the PI3K-AKT pathway downregulated following MECP2 deletion. Treatment with either KW-2449 or VPA, small molecules for the activation of PI3K-AKT signaling pathway, alleviated neuronal deficits and transcriptome changes in MECP2-KO human neuronal models.

CONCLUSIONS

These findings suggest that KW-2449 and VPA might be promising drugs for RTT treatment.

DEPCOD: a tool to detect and visualize co-evolution of protein domains

Proteins with similar phylogenetic patterns of conservation or loss across evolutionary taxa are strong candidates to work in the same cellular pathways or engage in physical or functional interactions. Our previously published tools implemented our method of normalized phylogenetic sequence profiling to detect functional associations between non-homologous proteins. However, many proteins consist of multiple protein domains subjected to different selective pressures, so using protein domain as the unit of analysis improves the detection of similar phylogenetic patterns. Here we analyze sequence conservation patterns across the whole tree of life for every protein domain from a set of widely studied organisms. The resulting new interactive webserver, DEPCOD (DEtection of Phylogenetically COrrelated Domains), performs searches with either a selected pre-defined protein domain or a user-supplied sequence as a query to detect other domains from the same organism that have similar conservation patterns. Top similarities on two evolutionary scales (the whole tree of life or eukaryotic genomes) are displayed along with known protein interactions and shared complexes, pathway enrichment among the hits, and detailed visualization of sources of detected similarities. DEPCOD reveals functional relationships between often non-homologous domains that could not be detected using whole-protein sequences. The web server is accessible at http://genetics.mgh.harvard.edu/DEPCOD.

Machine-learning of complex evolutionary signals improves classification of SNVs

Conservation is a strong predictor for the pathogenicity of single-nucleotide variants (SNVs). However, some positions that present complex conservation patterns across vertebrates stray from this paradigm. Here, we analyzed the association between complex conservation patterns and the pathogenicity of SNVs in the 115 disease-genes that had sufficient variant data. We show that conservation is not a one-rule-fits-all solution since its accuracy highly depends on the analyzed set of species and genes. For example, pairwise comparisons between the human and 99 vertebrate species showed that species differ in their ability to predict the clinical outcomes of variants among different genes using conservation. Furthermore, certain genes were less amenable for conservation-based variant prediction, while others demonstrated species that optimize prediction. These insights led to developing EvoDiagnostics, which uses the conservation against each species as a feature within a random-forest machine-learning classification algorithm. EvoDiagnostics outperformed traditional conservation algorithms, deep-learning based methods and most ensemble tools in every prediction-task, highlighting the strength of optimizing conservation analysis per-species and per-gene. Overall, we suggest a new and a more biologically relevant approach for analyzing conservation, which improves prediction of variant pathogenicity.