Integrative genomics reveals pathogenic mediator of valproate-induced neurodevelopmental disability

Sex-biased single-cell genetic landscape in mice with autism spectrum disorder

Autistic spectrum disorder (ASD) is a male-biased, heterogeneous neurodevelopmental disorder that affects approximately 1%-2% of the population. Prenatal exposure to valproic acid (VPA) is a recognized risk factor for ASD, but the cellular and molecular basis of VPA-induced ASD at the single-cell resolution is unclear. Here, we aim to compare the cellular and molecular differences in the hippocampus between male and female prenatal mice with ASD at the single-cell transcriptomic level. The transcriptomes of more than 45,000 cells are assigned to 12 major cell types, including neurons, glial cells, vascular cells, and immune cells. Cell type-specific genes with altered expression after prenatal VPA exposure are analyzed, and the largest number of differentially expressed genes (DEGs) are found in neurons, choroid plexus epithelial cells, and microglia. In microglia, several pathways related to inflammation are found in both males and females, including the tumor necrosis factor (TNF), nuclear factor kappa B (NF-κB), toll-like receptor (TLR), and mitogen-activated protein kinase (MAPK) signaling pathways, which are important for the induction of autistic-like behavior. Additionally, we note that several X-linked genes, including Bex1, Bex3, and Gria3, were among the male-specific DEGs of neurons. This pioneering study describes the landscape of the transcriptome in the hippocampus of autistic mice. The elucidation of sexual differences could provide innovative strategies for the prevention and treatment of ASD.

Enhancing social behavior in an autism spectrum disorder mouse model: investigating the underlying mechanisms of Lactiplantibacillus plantarum intervention

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting over 1% of the global population. Individuals with ASD often exhibit complex behavioral conditions, including significant social difficulties and repetitive behaviors. Moreover, ASD often co-occurs with several other conditions, including intellectual disabilities and anxiety disorders. The etiology of ASD remains largely unknown owing to its complex genetic variations and associated environmental risks. Ultimately, this poses a fundamental challenge for the development of effective ASD treatment strategies. Previously, we demonstrated that daily supplementation with the probiotic Lactiplantibacillus plantarum PS128 (PS128) alleviates ASD symptoms in children. However, the mechanism underlying this improvement in ASD-associated behaviors remains unclear. Here, we used a well-established ASD mouse model, induced by prenatal exposure to valproic acid (VPA), to study the physiological roles of PS128 in vivo. Overall, we showed that PS128 selectively ameliorates behavioral abnormalities in social and spatial memory in VPA-induced ASD mice. Morphological examination of dendritic architecture further revealed that PS128 facilitated the restoration of dendritic arborization and spine density in the hippocampus and prefrontal cortex of ASD mice. Notably, PS128 was crucial for restoring oxytocin levels in the paraventricular nucleus and oxytocin receptor signaling in the hippocampus. Moreover, PS128 alters the gut microbiota composition and increases the abundance of Bifidobacterium spp. and PS128-induced changes in Bifidobacterium abundance positively correlated with PS128-induced behavioral improvements. Together, our results show that PS128 treatment can effectively ameliorate ASD-associated behaviors and reinstate oxytocin levels in VPA-induced mice, thereby providing a promising strategy for the future development of ASD therapeutics.

Valproic Acid in Pregnancy Revisited: Neurobehavioral, Biochemical and Molecular Changes Affecting the Embryo and Fetus in Humans and in Animals: A Narrative Review

Valproic acid (VPA) is a very effective anticonvulsant and mood stabilizer with relatively few side effects. Being an epigenetic modulator, it undergoes clinical trials for the treatment of advanced prostatic and breast cancer. However, in pregnancy, it seems to be the most teratogenic antiepileptic drug. Among the proven effects are congenital malformations in about 10%. The more common congenital malformations are neural tube defects, cardiac anomalies, urogenital malformations including hypospadias, skeletal malformations and orofacial clefts. These effects are dose related; daily doses below 600 mg have a limited teratogenic potential. VPA, when added to other anti-seizure medications, increases the malformations rate. It induces malformations even when taken for indications other than epilepsy, adding to the data that epilepsy is not responsible for the teratogenic effects. VPA increases the rate of neurodevelopmental problems causing reduced cognitive abilities and language impairment. It also increases the prevalence of specific neurodevelopmental syndromes like autism (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). High doses of folic acid administered prior to and during pregnancy might alleviate some of the teratogenic effect of VPA and other AEDs. Several teratogenic mechanisms are proposed for VPA, but the most important mechanisms seem to be its effects on the metabolism of folate, SAMe and histones, thus affecting DNA methylation. VPA crosses the human placenta and was found at higher concentrations in fetal blood. Its concentrations in milk are low, therefore nursing is permitted. Animal studies generally recapitulate human data.

O’Brien T, Perucca P, Ge Z, Anderson A. Applications for Deep Learning in Epilepsy Genetic Research

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.