Modeling Somatic Mutations Associated With Neurodevelopmental Disorders in Human Brain Organoids

Deb, Bipan Kumar; Bateup, Helen S.

doi:10.3389/fnmol.2021.787243

Cited by 5 publications

(5 citation statements)

References 85 publications

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“…An early diagnosis of ASD is crucial for implementing interventional approaches on individuals with this disorder (Cidav et al, 2017 ). Multiple risk factors contribute to the ASD phenotype, including genetic, biological, psychosocial, and environmental contributors (Parenti et al, 2021 ; Deb and Bateup, 2022 ). This disorder is characterized by impairments in different areas of development, including deficits in social interactions and communication, by restricted/repetitive behaviors, and by sensory-perceptual alterations, which were added as an ASD diagnostic criterion (American Psychiatric Association, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

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

Alateyat

Cruz

Cernadas

et al. 2022

Front. Mol. Neurosci.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Alateyat

Cruz

Cernadas

et al. 2022

Front. Mol. Neurosci.

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“…Developmental disorders such as RTT are usually caused by functional mutations in the causative gene, and these dysfunctional disease models allow the etiological research and treatment strategies to be established. 71 , 72 , 73 , 74 In this paper, we genetically engineered exon regions of MeCP2 in iPSCs and developed a new RTT brain organoid model exhibiting complete dysfunction of MeCP2 with unreported defects in the neural rosette formation and neuronal specification related to the BMP signaling pathway.…”

Section: Discussionmentioning

confidence: 99%

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

Hong

Yoon

Park

et al. 2023

Ann Clin Transl Neurol

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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.

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“…Studies implicate germline, de novo , and somatic mutations in pathologies, including NDDs ( Acuna-Hidalgo et al, 2016 ; D'Gama and Walsh, 2018 ; Nishioka et al, 2019 ; Li et al, 2020 ; Deb and Bateup, 2021 ; Kim et al, 2021 ; Rashed et al, 2022 ). Several groups have recently explored the impact of different types of mutations, such as missense or truncating mutations, on disease onset and progression; however, mutations associated with NDDs were mostly rare.…”

Section: Ai ML Dl and Other Computational Methods In Tackling Nddsmentioning

confidence: 99%

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

Nussinov,

Yavuz,

Demirel

et al. 2024

Front. Cell Dev. Biol.

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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.

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Modeling Somatic Mutations Associated With Neurodevelopmental Disorders in Human Brain Organoids

Cited by 5 publications

References 85 publications

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

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

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

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

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