神経細胞移動異常症

The cerebral cortex is a multi-layered structure generated through the migration of neural precursors from their birthplace in the ventricular zone to their destination within the cortical plate. Neuronal migration defects are responsible for many human pathologies collectively called neuronal migration disorders, which include subcortical band heterotopia and cobblestone brain (COB) malformation. One example of a protein involved in a neuronal migration disorder is the echinoderm microtubule-associated protein-like 1 (EML1) protein, one of six members of the mammalian EML family. Absence of EML1 protein results in subcortical band heterotopia in mice and humans. Here, we report that the absence of the paralogous protein EML3 leads to delayed embryonic development and small size, and a COB-like phenotype with neuronal ectopias in the dorsal telencephalon. We found that EML3 is expressed in the neuroepithelium and meningeal mesenchyme when those tissues participate in pial basement membrane (PBM) formation. Transmission electron microscopy demonstrated that the extracellular matrix of the PBM is structurally abnormal in Eml3 null mice when the first radially migrating neurons arrive. The reduced structural integrity of the PBM leads to focal over-migration of neurons into the subarachnoid space. These findings strengthen the link between the EML protein family and cortical neuronal migration defects by identifying Eml3 as the first EML family member whose absence leads to over-migration of neuroblasts. Moreover, we report the first COB-like phenotype with PBM structural defects when a single microtubule-associated protein is deleted.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition including incorrect functioning in communication, social interaction, and repetitive behavior. Global prevalence is estimated as 1-2%, with a predominance of men. Different pre- and perinatal, environmental, immunological, neurobiological, genetic, and epigenetic factors are involved in the etiology of ASD. The study aims to analyze genetic abnormalities in patients with ASD according to data from the current literature. MATERIALS AND METHODS: Studies available in the PubMed and Google Scholar databases were chosen through a literature search. Only papers published from 2020, available as full-text publications in English, with studies conducted on humans, original papers, or meta-analyses were included. RESULTS AND DISCUSSION: The following types of genetic variation were identified: copy number variants, larger insertions, inversions, uniparental disomies, tandem repeat expansions, common single nucleotide polymorphisms, single nucleotide variants, short insertions/deletions, and mitochondrial variants. Epigenetic factors, like histone modifications, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid might play an important role in ASD predisposition. Genes identified in the review were mainly involved in neurodevelopment, synaptic formation, neuronal migration, neurotransmission, glial proliferation, ubiquitination, chromatin remodeling, or transcription. ASD is described as a component of the phenotype in fragile X syndrome, tuberous sclerosis complex, neurofibromatosis type 1, Angelman, Phelan-McDermid, Smith-Lemli-Opitz syndromes, and chromosome trisomies. Current guidelines for genetic diagnosis of ASD recommend performing directed genetic studies in the first line (like multiplex ligation-dependent probe amplification - MLPA, analysis of FMR1 gene), in case of a negative result, chromosomal microarray as a routine method, then next-generation sequencing (NGS) panel testing, WES (whole exome sequencing), or even WGS (whole genome sequencing) as the last test. CONCLUSIONS: Wider access to modern diagnostic methods has increased the number of ASD patients in whom the genetic etiology of the disorder has been uncovered. Knowledge of the genetic background would be applicable in the diagnosis, prevention, prognosis, and individualized treatment.

CMIP, a c-maf inducing protein that plays a key role in cytoskeletal remodeling, neuronal migration and synaptic formation, was first associated with specific language impairment and autism through the identification of a deletion in a single patient in 2012. Since then, only two additional individuals with CMIP deletions have been reported, both sharing features of autism and gastrointestinal features. However, a firm causal relationship between variants in CMIP and neurodevelopmental disorders has not yet been established. In this multicentre cohort study, we identified 25 individuals, from 17 unrelated families, with CMIP-related neurodevelopmental disorders, 22 of whom have not been previously reported. Of these, seven individuals carried heterozygous loss-of-function CMIP single-nucleotide variants, while the other 18 individuals had a complete or partial deletion of CMIP, some involving adjacent genes. The clinical phenotype was variable with a high prevalence of developmental delay (20/25), autism spectrum disorder features (13/25), attention-deficit/hyperactivity disorder features (11/25) and other psychiatric disorders (15/25). Epilepsy was present in nine individuals (9/25), of whom three had therapy-resistant seizures. To study the pathogenicity of CMIP variants, a cmip mutant zebrafish model carrying a premature stop codon was investigated. These mutants showed temperature-dependent altered locomotor activity suggestive of seizure-like behavior, which was confirmed by spontaneous epileptiform discharges in cmip+/- mutant zebrafish larvae. Our patient cohort and the zebrafish data establish CMIP as a gene implicated in neurodevelopmental and neuropsychiatric disorders. We recommend inclusion of CMIP in the genetic work-up of neurodevelopmental delay, with or without autism or psychiatric disorders and epilepsy.

OBJECTIVES: To clarify the upper limit of epilepsy onset in focal cortical dysplasia (FCD) Type II (FCD)-related epilepsy and to investigate factors affecting long-term surgical outcomes from an open-source database. METHODS: Patients with FCD Type II pathology were included from the Imaging Database for Epilepsy And Surgery. The primary outcome measure was the incidence of adult-onset FCD-drug resistant epilepsy (DRE). Secondary analyses focused on factors affecting long-term seizure-free outcomes after resective epilepsy surgery for FCD-DRE. RESULTS: Thirty-nine FCD-DRE patients were identified. Pathology was Type IIB (n = 30), Type IIA (n = 8), and Type II Not Otherwise Specified (n = 1). Only 5% had adult-onset DRE; 95% had onset before 15 years. Surgery was associated with 71.4% 5-year International League Against Epilepsy Class I outcome. FCD Type IIB was associated with high rate of seizure-free outcome (OR 13.0, 95% CI [1.36-124], p = 0.03). Focal-to-bilateral-tonic-clonic seizures, focal impaired consciousness seizures, prior status epilepticus, sex, hemisphere, age of epilepsy onset, and age of surgery were not associated with 5-year seizure-free outcome. DISCUSSION: Prior data show that ∼5% of FCDs are discovered incidentally. This study elucidates that the majority of FCD-DRE (including those patients treated as adults) is childhood-onset. These data demonstrate that FCD Type II (especially Type IIB) is associated with high rates of durable postsurgical seizure-free outcome.

BACKGROUND: This paper reports a genetic identification task using 3D convolutional neural network (3D-CNN) models applied to a proprietary 3D magnetic resonance imaging (MRI) dataset of patients with lissencephaly. Lissencephaly is a neuronal migration disorder caused by genetic mutations or deletions in which specific causative genes result in distinct morphological alterations in brain structure. OBJECTIVE: The objective of this study was to identify causative genes through image classification by analysing three-dimensional structural features of brain MRI using deep learning. METHODS: In our experiments, we extended representative CNN architectures to handle three-dimensional inputs and performed three-class classification targeting the primary causative genes, LIS1 and DCX, along with a category for other genetic variations. RESULTS: Our results demonstrated that the 3D-ResNet18 model achieved a mean classification accuracy of over 78%. Furthermore, to enhance the precision for primary genes, we introduced a decision-making process based on prediction probability thresholds. CONCLUSIONS: This approach yielded an average precision improvement of 4.67% for DCX and 4.84% for LIS1 across all the evaluated models.