Integrative analysis of genome-wide association study and chromosomal enhancer maps identified brain region related pathways associated with ADHD

Plexins: Navigating through the neural regulation and brain pathology

Plexins are a family of transmembrane receptors known for their diverse roles in neural development, axon guidance, neuronal migration, synaptogenesis, and circuit formation. Semaphorins are a class of secreted and membrane proteins that act as primary ligands for plexin receptors. Semaphorins play a crucial role in central nervous system (CNS) development by regulating processes such as axonal growth, neuronal positioning, and synaptic connectivity. Various types of semaphorins like sema3A, sema4A, sema4C, sema4D, and many more have a crucial role in developing brain diseases. Likewise, various evidence suggests that plexin receptors are of four types: plexin A, plexin B, plexin C, and plexin D. Plexins have emerged as crucial regulators of neurogenesis and neuronal development and connectivity. When bound to semaphorins, these receptors trigger two major networking cascades, namely Rho and Ras GTPase networks. Dysregulation of plexin networking has been implicated in a myriad of brain disorders, including autism spectrum disorder (ASD), Schizophrenia, Alzheimer's disease (AD), Parkinson's disease (PD), and many more. This review synthesizes findings from molecular, cellular, and animal model studies to elucidate the mechanisms by which plexins contribute to the pathogenesis of various brain diseases.

Shared molecular mechanisms and transdiagnostic potential of neurodevelopmental disorders and immune disorders

The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.