The role of histone methyltransferases in neurocognitive disorders associated with brain size abnormalities

Neuroimmune mechanisms in autism etiology - untangling a complex problem using human cellular models

Autism spectrum disorder (ASD) affects 1 in 36 people and is more often diagnosed in males than in females. Core features of ASD are impaired social interactions, repetitive behaviors and deficits in verbal communication. ASD is a highly heterogeneous and heritable disorder, yet its underlying genetic causes account only for up to 80% of the cases. Hence, a subset of ASD cases could be influenced by environmental risk factors. Maternal immune activation (MIA) is a response to inflammation during pregnancy, which can lead to increased inflammatory signals to the fetus. Inflammatory signals can cross the placenta and blood brain barriers affecting fetal brain development. Epidemiological and animal studies suggest that MIA could contribute to ASD etiology. However, human mechanistic studies have been hindered by a lack of experimental systems that could replicate the impact of MIA during fetal development. Therefore, mechanisms altered by inflammation during human pre-natal brain development, and that could underlie ASD pathogenesis have been largely understudied. The advent of human cellular models with induced pluripotent stem cell (iPSC) and organoid technology is closing this gap in knowledge by providing both access to molecular manipulations and culturing capability of tissue that would be otherwise inaccessible. We present an overview of multiple levels of evidence from clinical, epidemiological, and cellular studies that provide a potential link between higher ASD risk and inflammation. More importantly, we discuss how stem cell-derived models may constitute an ideal experimental system to mechanistically interrogate the effect of inflammation during the early stages of brain development.

Association Between COVID-19 and Neurological Diseases: Evidence from Large-Scale Mendelian Randomization Analysis and Single-Cell RNA Sequencing Analysis

Observational studies have suggested that SARS-CoV-2 infection increases the risk of neurological diseases, but it remains unclear whether the association is causal. The present study aims to evaluate the causal relationships between SARS-CoV-2 infections and neurological diseases and analyzes the potential routes of SARS-CoV-2 entry at the cellular level. We performed Mendelian randomization (MR) analysis with CAUSE method to investigate causal relationship of SARS-CoV-2 infections with neurological diseases. Then, we conducted single-cell RNA sequencing (scRNA-seq) analysis to obtain evidence of potential neuroinvasion routes by measuring SARS-CoV-2 receptor expression in specific cell subtypes. Fast gene set enrichment analysis (fGSEA) was further performed to assess the pathogenesis of related diseases. The results showed that the COVID-19 is causally associated with manic (delta_elpd, - 0.1300, Z-score: - 2.4; P = 0.0082) and epilepsy (delta_elpd: - 2.20, Z-score: - 1.80; P = 0.038). However, no significant effects were observed for COVID-19 on other traits. Moreover, there are 23 cell subtypes identified through the scRNA-seq transcriptomics data of epilepsy, and SARS-CoV-2 receptor TTYH2 was found to be specifically expressed in oligodendrocyte and astrocyte cell subtypes. Furthermore, fGSEA analysis showed that the cell subtypes with receptor-specific expression was related to methylation of lysine 27 on histone H3 (H3K27ME3), neuronal system, aging brain, neurogenesis, and neuron projection. In summary, this study shows causal links between SARS-CoV-2 infections and neurological disorders such as epilepsy and manic, supported by MR and scRNA-seq analysis. These results should be considered in further studies and public health measures on COVID-19 and neurological diseases.

CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs)

In the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6), the Genetics of Neurodevelopmental Disorders Lab in Padua proposed a new ID-challenge to give the opportunity of developing computational methods for predicting patient's phenotype and the causal variants. Eight research teams and 30 models had access to the phenotype details and real genetic data, based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. In this study we evaluate the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and causal variants. Finally, we asked to develop a method to find new possible genetic causes for patients without a genetic diagnosis. As already done for the CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (causative, putative pathogenic and contributing factors) were provided. Considering the overall clinical manifestation of our cohort, we give out the variant data and phenotypic traits of the 150 patients from CAGI5 ID-Challenge as training and validation for the prediction methods development.