English

T Ezashi

doi:10.13172/2052-9651-1-1-443

Abstract

Introduction Many neurodevelopmental disorders with intellectual disability and autism spectrum disorder syndromes have overlapping genetic and molecular connections. Effective treatments for such genetic neurodevelopmental disorders have not been established yet. Possible reasons include the lack of suitable disease models of human neurons and a limited understanding of the etiological and neurobiological mechanisms of the brain. The reprogramming technology of somatic cells allows the generation of induced pluripotent stem cells from the patients’ fibroblasts that subsequently differentiate into neuronal cells carrying the genetic variations that cause the specific disorders. Investigation into monogenic neurodevelopmental disorders has proved advantageous in their analysis, and there are a number of successful reports that have been published. This approach has a promising potential, but some technical caveats need to be addressed like the variability observed between cell lines that emerge after reprogramming. Another challenge involves differentiating the reprogrammed cells into cell types that are implicated in the disease phenotypes, with minimum heterogeneity and variability. Modelling monogenic neurological disorders with genetic mutations has been successful and is considered as a basic tool for establishing functional human neurons to reveal disease specific phenotypes. This critical review discusses the use of cellular reprogramming to study intellectual disability and neurodevelopmental disorders. Conclusion Methodology and analysis need to be improved to detect important differences between controls and patients’ cell lines. Technology has been improving over the last few years. Studies with improved induced pluripotent stem cell technologies in diseases that show highly penetrant and early onset syndromes, such as Smith-Magenis syndrome, PotockiLupski syndrome, Pitt-Hopkins syndrome and mental retardation type 1, will not only improve the understanding of these respective syndromes but also provide useful insights into the complex nature of brain disorders, including autism spectrum disorders.

Highlights

Many neurodevelopmental disorders with intellectual disability and autism spectrum disorder syndromes have overlapping genetic and molecular connections
retinoic acid-induced 1 (RAI1) is a transcription factor comprising of six exons, with most of the coding sequence located in the third exon where the majority of the Smith–Magenis syndrome (SMS) associated de novo mutations can be found, suggesting that a functional RAI1 gene dosage is responsible for the SMS phenotype7
Little is known about the genes regulated by RAI1 in the central nervous system; a recent study shows that RAI1 regulates the transcription of the circadian locomotor output cycles kaput (CLOCK)

Summary

Introduction

A number of neurodevelopmental disorders with intellectual disability harbour chromosomal microdeletions or microduplications. Microdeletions are deletions of a smaller scale, typically 1–3 Mb, and cannot be detected by conventional cytogenetic tests conducted with light microscopy These deletions and duplications can be discovered by array-based comparative genomic hybridisation, fluorescent in situ hybridisation or quantitative real-time polymerase chain reaction, if applicable. Monogenic disorders are diseases in which a single pair of genes is responsible for the emergence or absence of a particular group of symptoms or phenotypes Such monogenic neurodevelopmental disorders may aid in the understanding of molecular and cellular aspects of gene-brainphysiology and may provide insight into the neurobehavioural impacts of genetic variation. They may aid in the exploration of effective treatments for such syndromes by deciphering networks between the different neurophysiological phenotypes originating from the single causal gene. Disorders that are characterised with late onset are expected to have complications arising from the patients’ environment and the patients’ history of treatments, which may be the causes for the increased variation observed between the samples

Conclusion

Discussion