Wide spectrum of neuronal and network phenotypes in human stem cell-derived excitatory neurons with Rett syndrome-associated MECP2 mutations

Exploring the complexity of MECP2 function in Rett syndrome

Rett syndrome (RTT) is a neurodevelopmental disorder that is mainly caused by mutations in the methyl-DNA-binding protein MECP2. MECP2 is an important epigenetic regulator that plays a pivotal role in neuronal gene regulation, where it has been reported to function as both a repressor and an activator. Despite extensive efforts in mechanistic studies over the past two decades, a clear consensus on how MECP2 dysfunction impacts molecular mechanisms and contributes to disease progression has not been reached. Here, we review recent insights from epigenomic, transcriptomic and proteomic studies that advance our understanding of MECP2 as an interacting hub for DNA, RNA and transcription factors, orchestrating diverse processes that are crucial for neuronal function. By discussing findings from different model systems, we identify crucial epigenetic details and cofactor interactions, enriching our understanding of the multifaceted roles of MECP2 in transcriptional regulation and chromatin structure. These mechanistic insights offer potential avenues for rational therapeutic design for RTT.

Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) affect 4.7% of the global population and are associated with delays in brain development and a spectrum of impairments that can lead to lifelong disability and even mortality. Identification of biomarkers for accurate diagnosis and medications for effective treatment are lacking, in part due to the historical use of preclinical model systems that do not translate well to the clinic for neurological disorders, such as rodents and heterologous cell lines. Human-induced pluripotent stem cells (hiPSCs) are a promising in vitro system for modeling NDDs, providing opportunities to understand mechanisms driving NDDs in human neurons. Functional assays, including patch clamping, multielectrode array, and imaging-based assays, are popular tools employed with hiPSC disease models for disease investigation. Recent progress in machine learning (ML) algorithms also presents unprecedented opportunities to advance the NDD research process. In this review, we compare two-dimensional and three-dimensional hiPSC formats for disease modeling, discuss the applications of functional assays, and offer insights on incorporating ML into hiPSC-based NDD research and drug screening.

The power of human stem cell-based systems in the study of neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) affect 15% of children and are usually associated with intellectual disability, seizures, and autistic behaviors, among other neurological presentations. Mutations in a wide spectrum of gene families alter key stages of human brain development, leading to defects in neural circuits or brain architecture. Studies in animal systems have provided important insights into the pathobiology of several NDDs. Human stem cell technologies provide a complementary system that allows functional manipulation of human brain cells during developmental stages that would otherwise be inaccessible during human fetal brain development. Therefore, stem cell-based models advance our understanding of human brain development by revealing human-specific mechanisms contributing to the broad pathogenesis of NDDs. We provide a comprehensive overview of the latest research on two and three-dimensional human stem cell-based models. First, we discuss convergent cellular and molecular phenotypes across different NDDs that have been revealed by human iPSC systems. Next, we examine the contribution of in vitro human neural systems to the development of promising therapeutic strategies. Finally, we explore the potential of stem cell systems to draw mechanistic insight for the study of sex dimorphism within NDDs.

Calcium-Dependent Hyperexcitability in Human Stem Cell-Derived Rett Syndrome Neuronal Networks

Background

Mutations in MECP2 predominantly cause Rett syndrome and can be modeled in vitro using human stem cell-derived neurons. Patients with Rett syndrome have signs of cortical hyperexcitability, such as seizures. Human stem cell-derived MECP2 null excitatory neurons have smaller soma size and reduced synaptic connectivity but are also hyperexcitable due to higher input resistance. Paradoxically, networks of MECP2 null neurons show a decrease in the frequency of network bursts consistent with a hypoconnectivity phenotype. Here, we examine this issue.

Methods

We reanalyzed multielectrode array data from 3 isogenic MECP2 cell line pairs recorded over 6 weeks (n = 144). We used a custom burst detection algorithm to analyze network events and isolated a phenomenon that we termed reverberating super bursts (RSBs). To probe potential mechanisms of RSBs, we conducted pharmacological manipulations using bicuculline, EGTA-AM, and DMSO on 1 cell line (n = 34).

Results

RSBs, often misidentified as single long-duration bursts, consisted of a large-amplitude initial burst followed by several high-frequency, low-amplitude minibursts. Our analysis revealed that MECP2 null networks exhibited increased frequency of RSBs, which produced increased bursts compared with isogenic controls. Bicuculline or DMSO treatment did not affect RSBs. EGTA-AM selectively eliminated RSBs and rescued network burst dynamics.

Conclusions

During early development, MECP2 null neurons are hyperexcitable and produce hyperexcitable networks. This may predispose them to the emergence of hypersynchronic states that potentially translate into seizures. Network hyperexcitability depends on asynchronous neurotransmitter release that is likely driven by presynaptic Ca2+ and can be rescued by EGTA-AM to restore typical network dynamics.

Transcriptional Inhibition of the Mecp2 Promoter by MeCP2E1 and MeCP2E2 Isoforms Suggests Negative Auto-Regulatory Feedback that can be Moderated by Metformin

The epigenetic factor Methyl-CpG-Binding Protein 2 (MeCP2) is a nuclear protein that binds methylated DNA molecules (both 5-methylcytosine and 5-hydroxymethylcytosine) and controls gene transcription. MeCP2 is an important transcription factor that acts in a dose-dependent manner in the brain; thus, its optimal expression level in brain cells is important. As such, its deregulated expression, as well as gain- or loss-of-function mutation, lead to impaired neurodevelopment, and compromised structure and function of brain cells, particularly in neurons. Studies from others and us have characterized two well-recognized MeCP2 isoforms: MeCP2E1 and MeCP2E2. We have reported that in Daoy medulloblastoma brain cells, MeCP2E2 overexpression leads to MeCP2E1 protein degradation. Whether MeCP2 isoforms regulate the Mecp2 promoter regulatory elements remains unexplored. We previously showed that in Daoy cells, metformin (an anti-diabetic drug) induces MECP2E1 transcripts. However, possible impact of metformin on the Mecp2 promoter activity was not studied. Here, we generated stably transduced Daoy cell reporters to express EGFP driven by the Mecp2 promoter. Transduced cells were sorted into four EGFP-expressing groups (R4-to-R7) with different intensities of EGFP expression. Our results confirm that the Mecp2 promoter is active in Daoy cells, and that overexpression of either isoform inhibits the Mecp2 promoter activity, as detected by flow cytometry and luciferase reporter assays. Interestingly, metformin partially relieved the inhibitory effect of MeCP2E1 on the Mecp2 promoter, detected by flow cytometry. Taken together, our data provide important insight towards the regulation of MeCP2 isoforms at the promoter level, which might have biological relevance to the neurobiology of the brain.

Associations of behavioral problems with white matter circuits connecting to the frontal lobes in school-aged children born at term and preterm

Introduction

This study investigated whether behavioral problems in children were associated with fractional anisotropy (FA) of white matter tracts connecting from other brain regions to right and left frontal lobes. We considered internalizing and externalizing behavioral problems separately and contrasted patterns of associations in children born at term and very preterm.

Methods

Parents completed the Child Behavior Checklist/6-18 questionnaire to quantify behavioral problems when their children were age 8 years (N=36 FT and 37 PT). Diffusion magnetic resonance scans were collected at the same age and analyzed using probabilistic tractography. We used multiple linear regression to investigate the strength of association between age-adjusted T-scores of internalizing and externalizing problems and mean fractional anisotropy (mean-FA) of right and left uncinate, arcuate, and anterior thalamic radiations, controlling for birth group and sex.

Results

Regression models predicting internalizing T-scores from mean-FA found significant group-by-tract interactions for the left and right arcuate and right uncinate. Internalizing scores were negatively associated with mean-FA of left and right arcuate only in children born at term (pleft AF =0.01, pright AF =0.01). Regression models predicting externalizing T-scores from mean-FA found significant group-by-tract interactions for the left arcuate and right uncinate. Externalizing scores were negatively associated with mean-FA of right uncinate in children born at term (pright UF =0.01) and positively associated in children born preterm (pright UF preterm =0.01). Other models were not significant.

Conclusions

In this sample of children with scores for behavioral problems across the full range, internalizing and externalizing behavioral problems were negatively associated with mean-FA of white matter tracts connecting to frontal lobes in children born at term; externalizing behavioral problems were positively associated with mean-FA of the right uncinate in children born preterm. The different associations by birth group suggest that the neurobiology of behavioral problems differs in the two birth groups.

Network size affects the complexity of activity in human iPSC-derived neuronal populations

Multi-electrode recording of neural activity in cultures offer opportunities for understanding how the structure of a network gives rise to function. Although it is hypothesized that network size is critical for determining the dynamics of activity, this relationship in human neural cultures remains largely unexplored. By applying new methods for analyzing neural activity to human iPSC derived cultures at either low-densities or high-densities, we uncovered the significant impacts that neuron number has on the individual neurophysiological properties of cells (such as firing rates), the collective behavior of the networks these cultures formed (as measured by entropy), and the relationship between the two. As a result, simply changing the densities of neurons generated dynamics and network behavior that differed not just in degree, but in kind. Beyond revealing the relationship between network structure and function, our findings provide a novel analytical framework to study diseases where network level activity is affected.

An in silico and in vitro human neuronal network model reveals cellular mechanisms beyond NaV1.1 underlying Dravet syndrome

Human induced pluripotent stem cell (hiPSC)-derived neuronal networks on multi-electrode arrays (MEAs) provide a unique phenotyping tool to study neurological disorders. However, it is difficult to infer cellular mechanisms underlying these phenotypes. Computational modeling can utilize the rich dataset generated by MEAs, and advance understanding of disease mechanisms. However, existing models lack biophysical detail, or validation and calibration to relevant experimental data. We developed a biophysical in silico model that accurately simulates healthy neuronal networks on MEAs. To demonstrate the potential of our model, we studied neuronal networks derived from a Dravet syndrome (DS) patient with a missense mutation in SCN1A, encoding sodium channel NaV1.1. Our in silico model revealed that sodium channel dysfunctions were insufficient to replicate the in vitro DS phenotype, and predicted decreased slow afterhyperpolarization and synaptic strengths. We verified these changes in DS patient-derived neurons, demonstrating the utility of our in silico model to predict disease mechanisms.

Transition from Animal-Based to Human Induced Pluripotent Stem Cells (iPSCs)-Based Models of Neurodevelopmental Disorders: Opportunities and Challenges

Neurodevelopmental disorders (NDDs) arise from the disruption of highly coordinated mechanisms underlying brain development, which results in impaired sensory, motor and/or cognitive functions. Although rodent models have offered very relevant insights to the field, the translation of findings to clinics, particularly regarding therapeutic approaches for these diseases, remains challenging. Part of the explanation for this failure may be the genetic differences-some targets not being conserved between species-and, most importantly, the differences in regulation of gene expression. This prompts the use of human-derived models to study NDDS. The generation of human induced pluripotent stem cells (hIPSCs) added a new suitable alternative to overcome species limitations, allowing for the study of human neuronal development while maintaining the genetic background of the donor patient. Several hIPSC models of NDDs already proved their worth by mimicking several pathological phenotypes found in humans. In this review, we highlight the utility of hIPSCs to pave new paths for NDD research and development of new therapeutic tools, summarize the challenges and advances of hIPSC-culture and neuronal differentiation protocols and discuss the best way to take advantage of these models, illustrating this with examples of success for some NDDs.