Chromosomal Microarray Analysis in Turkish Patients with Unexplained Developmental Delay and Intellectual Developmental Disorders

Identification and bioinformatics analysis of a novel variant in the HERC2 gene in a patient with intellectual developmental disorder

HERC2-associated neurodevelopmental-disorders(NDD) encompass a cluster of medical conditions that arise from genetic mutations occurring within the HERC2 gene. These disorders can manifest a spectrum of symptoms that impact the brain and nervous system, including delayed psychomotor development, severe mental retardation, seizures and autistic features. Whole-Exome-Sequencing(WES) was performed on a ten-year-old male patient referred to the genetic center for genetic analysis. Blood samples were collected from the proband, his parents, and his sister to extract DNA. PCR-Sanger-sequencing was utilized to validate the findings obtained from WES. In order to obtain a more thorough understanding of the impact of the mutation, an extensive analysis was conducted using bioinformatics tools. WES data analysis identified a homozygous single nucleotide change(C > T) at position c14215 located in exon ninety-two of the HERC2 gene (NC_000015.10(NM_004667.6):c.14215C > T). The absence of this mutation among our cohort composed of four hundred normal healthy adults from the same ethnic group, and its absence in any other population database, confirms the pathogenicity of the mutation. This study revealed that the substitution of arginine with a stop codon within the Hect domain caused a premature stop codon at position 4739(p.Arg4739Ter). This mutation significantly results in the production of a truncated HERC2 protein with an incomplete HECT domain. In the final stage of ubiquitin attachment, HECT E3 ubiquitin ligases play a catalytic role by creating a thiolester intermediate using their conserved catalytic cysteine (Cys4762). This intermediate is formed before ubiquitin is transferred to a substrate protein. The truncation of the HERC2 protein is expected to disrupt its ability to perform this function, which could potentially hinder important regulatory processes related to the development and maintenance of synapses. The identification of a novel pathogenic variant, NC_000015.10(NM_004667.6):c.14215C > T, located within the ninety-two exon of the HERC2 gene, is notable for its association with an autosomal recessive inheritance pattern in cases of Intellectual Developmental Disorder(IDD). In the end, this variant could potentially play a part in the underlying mechanisms leading to the onset of intellectual developmental disorder.

ARHGEF5 binds Drebrin and affects α-tubulin acetylation to direct neuronal morphogenesis and migration during mouse brain development

Rho guanine nucleotide exchange factors (Rho GEFs) activate Rho GTPases, which act as molecular switches regulating various essential cellular functions. This study investigated the role of ARHGEF5, a Rho GEF known for its involvement in cell migration and invasion processes, in the context of brain development. We found that ARHGEF5 is essential for dendrite development during the early stages of neuronal growth. We also discovered that ARHGEF5 binds to Drebrin E, which is vital for coordinating actin and microtubule dynamics, and facilitates the interaction between Drebrin E and Cyclin-dependent kinase 5, which phosphorylates Drebrin E. Notably, ARHGEF5 deficiency resulted in a decrease in acetylated α-tubulin levels, and the expression of an α-tubulin acetylation mimetic mutant (K40Q) rescued the defects in dendrite development and neuronal migration, suggesting ARHGEF5's role in modulating microtubule stability. Additionally, ARHGEF5 was shown to influence Golgi positioning in the leading processes of migrating cortical neurons during brain development. Our study suggests that ARHGEF5 plays a crucial role in integrating cytoskeletal dynamics with neuronal morphogenesis and migration processes during brain development.

Genetic and phenotypic analysis of 225 Chinese children with developmental delay and/or intellectual disability using whole-exome sequencing

Developmental delay (DD), or intellectual disability (ID) is a very large group of early onset disorders that affects 1-2% of children worldwide, which have diverse genetic causes that should be identified. Genetic studies can elucidate the pathogenesis underlying DD/ID. In this study, whole-exome sequencing (WES) was performed on 225 Chinese DD/ID children (208 cases were sequenced as proband-parent trio) who were classified into seven phenotype subgroups. The phenotype and genomic data of patients with DD/ID were further retrospectively analyzed. There were 96/225 (42.67%; 95% confidence interval [CI] 36.15-49.18%) patients were found to have causative single nucleotide variants (SNVs) and small insertions/deletions (Indels) associated with DD/ID based on WES data. The diagnostic yields among the seven subgroups ranged from 31.25 to 71.43%. Three specific clinical features, hearing loss, visual loss, and facial dysmorphism, can significantly increase the diagnostic yield of WES in patients with DD/ID (P = 0.005, P = 0.005, and P = 0.039, respectively). Of note, hearing loss (odds ratio [OR] = 1.86%; 95% CI = 1.00-3.46, P = 0.046) or abnormal brainstem auditory evoked potential (BAEP) (OR = 1.91, 95% CI = 1.02-3.50, P = 0.042) was independently associated with causative genetic variants in DD/ID children. Our findings enrich the variation spectrums of SNVs/Indels associated with DD/ID, highlight the value genetic testing for DD/ID children, stress the importance of BAEP screen in DD/ID children, and help to facilitate early diagnose, clinical management and reproductive decisions, improve therapeutic response to medical treatment.

Combining chromosomal microarray and clinical exome sequencing for genetic diagnosis of intellectual disability

Despite the current widespread use of chromosomal microarray analysis (CMA) and exome/genome sequencing for the genetic diagnosis of unexplained intellectual disability (ID) in children, gaining improved diagnostic yields and defined guidelines remains a significant challenge. This is a cohort study of children with unexplained ID. We analyzed the diagnostic yield and its correlation to clinical phenotypes in children with ID who underwent concurrent CMA and clinical exome sequencing (CES). A total of 154 children were included (110 [71.4%] male; mean [SD] age, 51.9 [23.1] months). The overall diagnosis yield was 26.0-33.8%, with CMA contributing 12.3-14.3% and CES contributing 13.6-19.4%, showing no significant difference. The diagnostic rate was significantly higher when gross motor delay (odds ratio, 6.69; 95% CI, 3.20-14.00; P < 0.001), facial dysmorphism (odds ratio, 9.34; 95% CI 4.29-20.30; P < 0.001), congenital structural anomaly (odds ratio 3.62; 95% CI 1.63-8.04; P = 0.001), and microcephaly or macrocephaly (odds ratio 4.87; 95% CI 2.05-11.60; P < 0.001) were presented. Patients with only ID without any other concomitant phenotype (63/154, 40.9%) exhibited a 6.3-11.1% diagnostic rate.

Clinical and Genetic Characteristics of Patients with Unexplained Intellectual Disability/Developmental Delay without Epilepsy

Introduction

Global developmental delay (DD), intellectual disability (ID), and autism spectrum disorder (ASD) are mainly evaluated under the neurodevelopmental disorder framework. In this study, we aimed to determine the genetic diagnosis yield using step-by-step genetic analysis in 38 patients with unexplained ID/DD and/or ASD.

Methods

In 38 cases (27 male, 11 female) with unexplained ID/DD and/or ASD, chromosomal microarray (CMA) analysis, clinical exome sequencing (CES), and whole-exome sequencing (WES) analysis were applied, respectively.

Results

We found a diagnostic rate of only CMA analysis as 21% (8/38) presenting 8 pathogenic and likely pathogenic CNVs. The rate of patients diagnosed with CES/WES methods was 32.2% (10/31). When all pathogenic and likely pathogenic variants were evaluated, the diagnosis rate was 44.7% (17/38). A dual diagnosis was obtained in a case with 16p11.2 microduplication and de novo SNV. We identified eight novel variants: TUBA1A (c.787C>G), TMEM63A (c.334-2A>G), YY1AP1 (c.2051_2052del), ABCA13 (c.12064C>T), ABCA13 (c.13187G>A), USP9X (c.1189T>C), ANKRD17 (c.328_330dup), and GRIA4 (c.17G>A).

Conclusion

We present diagnostic rates of a complementary approach to genetic analysis (CMA, CES, and WES). The combined use of genetic analysis methods in unexplained ID/DD and/or ASD cases has contributed significantly to diagnosis rates. Also, we present detailed clinical characteristics to improve genotype-phenotype correlation in the literature for rare and novel variants.

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer's Disease

Alzheimer's disease (AD) is a progressive disease that leads to irreversible behavioral changes, erratic emotions, and loss of motor skills. These conditions make people with AD hard or almost impossible to take care of. Multiple internal and external pathological factors may affect or even trigger the initiation and progression of AD. DNA methylation is one of the most effective regulatory roles during AD pathogenesis, and pathological methylation alterations may be potentially different in the various brain structures of people with AD. Although multiple loci associated with AD initiation and progression have been identified, the spatial distribution patterns of AD-associated DNA methylation in the brain have not been clarified. According to the systematic methylation profiles on different structural brain regions, we applied multiple machine learning algorithms to investigate such profiles. First, the profile on each brain region was analyzed by the Boruta feature filtering method. Some important methylation features were extracted and further analyzed by the max-relevance and min-redundancy method, resulting in a feature list. Then, the incremental feature selection method, incorporating some classification algorithms, adopted such list to identify candidate AD-associated loci at methylation with structural specificity, establish a group of quantitative rules for revealing the effects of DNA methylation in various brain regions (i.e., four brain structures) on AD pathogenesis. Furthermore, some efficient classifiers based on essential methylation sites were proposed to identify AD samples. Results revealed that methylation alterations in different brain structures have different contributions to AD pathogenesis. This study further illustrates the complex pathological mechanisms of AD.