Modeling Somatic Mutations Associated With Neurodevelopmental Disorders in Human Brain Organoids

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

MeCP2 dysfunction prevents proper BMP signaling and neural progenitor expansion in brain organoid

OBJECTIVES

Sporadic mutations in MeCP2 are a hallmark of Rett syndrome (RTT). Many RTT brain organoid models have exhibited pathogenic phenotypes such as decreased spine density and small size of soma with altered electrophysiological signals. However, previous models are mainly focused on the phenotypes observed in the late phase and rarely provide clues for the defect of neural progenitors which generate different types of neurons and glial cells.

METHODS

We newly established the RTT brain organoid model derived from MeCP2-truncated iPS cells which were genetically engineered by CRISPR/Cas9 technology. By immunofluorescence imaging, we studied the development of NPC pool and its fate specification into glutamatergic neurons or astrocytes in RTT organoids. By total RNA sequencing, we investigated which signaling pathways were altered during the early brain development in RTT organoids.

RESULTS

Dysfunction of MeCP2 caused the defect of neural rosette formation in the early phase of cortical development. In total transcriptome analysis, BMP pathway-related genes are highly associated with MeCP2 depletion. Moreover, levels of pSMAD1/5 and BMP target genes are excessively increased, and treatment of BMP inhibitors partially rescues the cell cycle progression of neural progenitors. Subsequently, MeCP2 dysfunction reduced the glutamatergic neurogenesis and induced overproduction of astrocytes. Nevertheless, early inhibition of BMP pathway rescued VGLUT1 expression and suppressed astrocyte maturation.

INTERPRETATION

Our results demonstrate that MeCP2 is required for the expansion of neural progenitor cells by modulating BMP pathway at early stages of development, and this influence persists during neurogenesis and gliogenesis at later stages of brain organoid development.

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.

A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems

Atypical sensory processing described in autism spectrum disorders (ASDs) frequently cascade into behavioral alterations: isolation, aggression, indifference, anxious/depressed states, or attention problems. Predictive machine learning models might refine the statistical explorations of the associations between them by finding out how these dimensions are related. This study investigates whether behavior problems can be predicted using sensory processing abilities. Participants were 72 children and adolescents (21 females) diagnosed with ASD, aged between 6 and 14 years (M = 7.83 years; SD = 2.80 years). Parents of the participants were invited to answer the Sensory Profile 2 (SP2) and the Child Behavior Checklist (CBCL) questionnaires. A collection of 26 supervised machine learning regression models of different families was developed to predict the CBCL outcomes using the SP2 scores. The most reliable predictions were for the following outcomes: total problems (using the items in the SP2 touch scale as inputs), anxiety/depression (using avoiding quadrant), social problems (registration), and externalizing scales, revealing interesting relations between CBCL outcomes and SP2 scales. The prediction reliability on the remaining outcomes was “moderate to good” except somatic complaints and rule-breaking, where it was “bad to moderate.” Linear and ridge regression achieved the best prediction for a single outcome and globally, respectively, and gradient boosting machine achieved the best prediction in three outcomes. Results highlight the utility of several machine learning models in studying the predictive value of sensory processing impairments (with an early onset) on specific behavior alterations, providing evidences of relationship between sensory processing impairments and behavior problems in ASD.

Brain somatic mutations as RNA therapeutic targets in neurological disorders

Research into the genetic etiology of a neurological disorder can provide directions for genetic diagnosis and targeted therapy. In the past, germline mutations, which are transmitted from parents or newly arise from parental germ cells, were considered as major genetic causes of neurological disorders. However, recent evidence has shown that somatic mutations in the brain, which can arise from neural stem cells during development or over aging, account for a significant number of brain disorders, ranging from neurodevelopmental, neurodegenerative, and neuropsychiatric to neoplastic disease. Moreover, the identification of disease-causing somatic mutations or mutated genes has provided new insights into molecular pathogenesis and unveiled potential therapeutic targets for treating neurological disorders that have few, or no, therapeutic options. RNA therapeutics, including antisense oligonucleotide (ASO) and small interfering RNA (siRNA), are emerging as promising therapeutic tools for treating genetic neurological disorders. As the number of approved and investigational ASO and siRNA drugs for neurological disorders associated with germline mutations increases, they may also prove to be attractive modalities for treating neurologic disorders resulting from somatic mutations. In this perspective, we highlight several neurological diseases caused by brain somatic mutations and discuss the potential role of RNA therapeutics in these conditions.