Deep exome sequencing identifies enrichment of deleterious mosaic variants in neurodevelopmental disorder genes and mitochondrial tRNA regions in bipolar disorder

Rare nonsynonymous germline and mosaic de novo variants in Japanese patients with schizophrenia

AIM

Whole-exome sequencing (WES) studies have revealed that germline de novo variants (gDNVs) contribute to the genetic etiology of schizophrenia. However, the contribution of mosaic DNVs (mDNVs) to the risk of schizophrenia remains to be elucidated. In the present study, we systematically investigated the gDNVs and mDMVs that contribute to the genetic etiology of schizophrenia in a Japanese population.

METHODS

We performed deep WES (depth: 460×) of 73 affected offspring and WES (depth: 116×) of 134 parents from 67 families with schizophrenia. Prioritized rare nonsynonymous gDNV and mDNV candidates were validated using Sanger sequencing and ultra-deep targeted amplicon sequencing (depth: 71,375×), respectively. Subsequently, we performed a Gene Ontology analysis of the gDNVs and mDNVs to obtain biological insights. Lastly, we selected DNVs in known risk genes for psychiatric and neurodevelopmental disorders.

RESULTS

We identified 62 gDNVs and 98 mDNVs. The Gene Ontology analysis of mDNVs implicated actin filament and actin cytoskeleton as candidate biological pathways. There were eight DNVs in known risk genes: splice region gDNVs in AKAP11 and CUL1; a frameshift gDNV in SHANK1; a missense gDNV in SRCAP; missense mDNVs in CTNNB1, GRIN2A, and TSC2; and a nonsense mDNV in ZFHX4.

CONCLUSION

Our results suggest the potential contributions of rare nonsynonymous gDNVs and mDNVs to the genetic etiology of schizophrenia. This is the first report of the mDNVs in schizophrenia trios, demonstrating their potential relevance to schizophrenia pathology.

Increased somatic mosaicism in autosomal and X chromosomes for suicide death

Mosaic chromosomal alterations (mCAs) are classified as mosaic deletions (loss), copy-neutral loss of heterozygosity (CN-LOH), and duplications (gain), attracting special attention as biological aging-related acquired genetic alterations. While these mCAs have been linked with aging and various diseases, no study has investigated their association with suicide risk which is associated with abnormal biological aging. Here, we examined the association between suicide deaths and mCAs, including mosaic loss of the X (mLOX) and Y chromosomes, by leveraging blood-derived single nucleotide polymorphism-array data. The first (410 suicide decedents and 88,870 controls) and the second (363 suicide decedents and 88,870 controls) cohorts were analyzed and integrated using meta-analyses (773 suicide decedents and 177,740 controls). Total mCAs in autosomal chromosomes were significantly increased in suicide (p = 1.28 × 10-6, odds ratio [OR] = 1.78), mostly driven by loss (p = 4.05 × 10-9, OR = 2.70) and gain (p = 1.08 × 10-3, OR = 2.23). mLOX were significantly increased in female suicide (p = 2.66 × 10-21, OR = 4.00). The directions of effects of all mCAs in autosomal and sex chromosomes on suicide were the same in the first and second sets. Subgroup analyses suggest that our findings were mostly driven by suicide itself, and not confounded by comorbid psychiatric disorders or physical diseases, smoking status, sample location, or postmortem sample status. In conclusion, we provide the first evidence for aberrant mCAs in somatic autosomal and X chromosomes in suicide, which may contribute to an improved understanding of the genomic pathophysiology underlying suicide.

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation

BACKGROUND

Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases.

RESULTS

We collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays.

CONCLUSIONS

Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation

Background

Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of regulatory programs this variation affects can shed light on the apparatuses of human diseases.

Results

We collected epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we constructed networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks served as the base for a rich series of analyses, through which we demonstrated their temporal dynamics and enrichment for various disease-associated variants. We applied the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrated methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays.

Conclusions

Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes. This includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.

Autism Spectrum Disorder: Brain Areas Involved, Neurobiological Mechanisms, Diagnoses and Therapies

Autism spectrum disorder (ASD), affecting over 2% of the pre-school children population, includes an important fraction of the conditions accounting for the heterogeneity of autism. The disease was discovered 75 years ago, and the present review, based on critical evaluations of the recognized ASD studies from the beginning of 1990, has been further developed by the comparative analyses of the research and clinical reports, which have grown progressively in recent years up to late 2023. The tools necessary for the identification of the ASD disease and its related clinical pathologies are genetic and epigenetic mutations affected by the specific interaction with transcription factors and chromatin remodeling processes occurring within specific complexes of brain neurons. Most often, the ensuing effects induce the inhibition/excitation of synaptic structures sustained primarily, at dendritic fibers, by alterations of flat and spine response sites. These effects are relevant because synapses, established by specific interactions of neurons with glial cells, operate as early and key targets of ASD. The pathology of children is often suspected by parents and communities and then confirmed by ensuing experiences. The final diagnoses of children and mature patients are then completed by the combination of neuropsychological (cognitive) tests and electro-/magneto-encephalography studies developed in specialized centers. ASD comorbidities, induced by processes such as anxieties, depressions, hyperactivities, and sleep defects, interact with and reinforce other brain diseases, especially schizophrenia. Advanced therapies, prescribed to children and adult patients for the control of ASD symptoms and disease, are based on the combination of well-known brain drugs with classical tools of neurologic and psychiatric practice. Overall, this review reports and discusses the advanced knowledge about the biological and medical properties of ASD.

Genomic variation in neurons

Neurons born during the fetal period have extreme longevity and survive until the death of the individual because the human brain has highly limited tissue regeneration. The brain is comprised of an enormous variety of neurons each exhibiting different morphological and physiological characteristics and recent studies have further reported variations in their genome including chromosomal abnormalities, copy number variations, and single nucleotide mutations. During the early stages of brain development, the increasing number of neurons generated at high speeds has been proposed to lead to chromosomal instability. Additionally, mutations in the neuronal genome can occur in the mature brain. This observed genomic mosaicism in the brain can be produced by multiple endogenous and environmental factors and careful analyses of these observed variations in the neuronal genome remain central for our understanding of the genetic basis of neurological disorders.