De novo variants predicting haploinsufficiency for DIP2C are associated with expressive speech delay.

The disconnected (disco)-interacting protein 2 (DIP2) gene was first identified in D. melanogaster and contains a DNA methyltransferase-associated protein 1 (DMAP1) binding domain, Acyl-CoA synthetase domain and AMP-binding sites. DIP2 regulates axonal bifurcation of the mushroom body neurons in D. melanogaster and is required for axonal regeneration in the neurons of C. elegans. The DIP2 homologues in vertebrates, Disco-interacting protein 2 homolog A (DIP2A), Disco-interacting protein 2 homolog B (DIP2B), and Disco-interacting protein 2 homolog C (DIP2C), are highly conserved and expressed widely in the central nervous system. Although there is evidence that DIP2C plays a role in cognition, reports of pathogenic variants in these genes are rare and their significance is uncertain. We present 23 individuals with heterozygous DIP2C variants, all manifesting developmental delays that primarily affect expressive language and speech articulation. Eight patients had de novo variants predicting loss-of-function in the DIP2C gene, two patients had de novo missense variants, three had paternally inherited loss of function variants and six had maternally inherited loss-of-function variants, while inheritance was unknown for four variants. Four patients had cardiac defects (hypertrophic cardiomyopathy, atrial septal defects, and bicuspid aortic valve). Minor facial anomalies were inconsistent but included a high anterior hairline with a long forehead, broad nasal tip, and ear anomalies. Brainspan analysis showed elevated DIP2C expression in the human neocortex at 10-24 weeks after conception. With the cases presented herein, we provide phenotypic and genotypic data supporting the association between loss-of-function variants in DIP2C with a neurocognitive phenotype.

Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution.

Human brain development is underpinned by cellular and molecular reconfigurations continuing into the third decade of life. To reveal cell dynamics orchestrating neural maturation, we profiled human prefrontal cortex gene expression and chromatin accessibility at single-cell resolution from gestation to adulthood. Integrative analyses define the dynamic trajectories of each cell type, revealing major gene expression reconfiguration at the prenatal-to-postnatal transition in all cell types followed by continuous reconfiguration into adulthood and identifying regulatory networks guiding cellular developmental programs, states, and functions. We uncover links between expression dynamics and developmental milestones, characterize the diverse timing of when cells acquire adult-like states, and identify molecular convergence from distinct developmental origins. We further reveal cellular dynamics and their regulators implicated in neurological disorders. Finally, using this reference, we benchmark cell identities and maturation states in organoid models. Together, this captures the dynamic regulatory landscape of human cortical development.

Alu-minating the Mechanisms Underlying Primate Cortex Evolution.

The higher-order cognitive functions observed in primates correlate with the evolutionary enhancement of cortical volume and folding, which in turn are driven by the primate-specific expansion of cellular diversity in the developing cortex. Underlying these changes is the diversification of molecular features including the creation of human and/or primate-specific genes, the activation of specific molecular pathways, and the interplay of diverse layers of gene regulation. We review and discuss evidence for connections between Alu elements and primate brain evolution, the evolutionary milestones of which are known to coincide along primate lineages. Alus are repetitive elements that contribute extensively to the acquisition of novel genes and the expansion of diverse gene regulatory layers, including enhancers, alternative splicing, RNA editing, and microRNA pathways. By reviewing the impact of Alus on molecular features linked to cortical expansions or gyrification or implications in cognitive deficits, we suggest that future research focusing on the role of Alu-derived molecular events in the context of brain development may greatly advance our understanding of higher-order cognitive functions and neurologic disorders.

De Novo ZMYND8 variants result in an autosomal dominant neurodevelopmental disorder with cardiac malformations.

PURPOSE: ZMYND8 encodes a multidomain protein that serves as a central interactive hub for coordinating critical roles in transcription regulation, chromatin remodeling, regulation of super-enhancers, DNA damage response and tumor suppression. We delineate a novel neurocognitive disorder caused by variants in the ZMYND8 gene. METHODS: An international collaboration, exome sequencing, molecular modeling, yeast two-hybrid assays, analysis of available transcriptomic data and a knockdown Drosophila model were used to characterize the ZMYND8 variants. RESULTS: ZMYND8 variants were identified in 11 unrelated individuals; 10 occurred de novo and one suspected de novo; 2 were truncating, 9 were missense, of which one was recurrent. The disorder is characterized by intellectual disability with variable cardiovascular, ophthalmologic and minor skeletal anomalies. Missense variants in the PWWP domain of ZMYND8 abolish the interaction with Drebrin and missense variants in the MYND domain disrupt the interaction with GATAD2A. ZMYND8 is broadly expressed across cell types in all brain regions and shows highest expression in the early stages of brain development. Neuronal knockdown of the DrosophilaZMYND8 ortholog results in decreased habituation learning, consistent with a role in cognitive function. CONCLUSION: We present genomic and functional evidence for disruption of ZMYND8 as a novel etiology of syndromic intellectual disability.

Comprehensive evaluation of deconvolution methods for human brain gene expression.

Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.

Evolutionary dynamics of circular RNAs in primates.

Many primate genes produce circular RNAs (circRNAs). However, the extent of circRNA conservation between closely related species remains unclear. By comparing tissue-specific transcriptomes across over 70 million years of primate evolution, we identify that within 3 million years circRNA expression profiles diverged such that they are more related to species identity than organ type. However, our analysis also revealed a subset of circRNAs with conserved neural expression across tens of millions of years of evolution. By comparing to species-specific circRNAs, we identified that the downstream intron of the conserved circRNAs display a dramatic lengthening during evolution due to the insertion of novel retrotransposons. Our work provides comparative analyses of the mechanisms promoting circRNAs to generate increased transcriptomic complexity in primates.

Supervised application of internal validation measures to benchmark dimensionality reduction methods in scRNA-seq data.

A typical single-cell RNA sequencing (scRNA-seq) experiment will measure on the order of 20 000 transcripts and thousands, if not millions, of cells. The high dimensionality of such data presents serious complications for traditional data analysis methods and, as such, methods to reduce dimensionality play an integral role in many analysis pipelines. However, few studies have benchmarked the performance of these methods on scRNA-seq data, with existing comparisons assessing performance via downstream analysis accuracy measures, which may confound the interpretation of their results. Here, we present the most comprehensive benchmark of dimensionality reduction methods in scRNA-seq data to date, utilizing over 300 000 compute hours to assess the performance of over 25 000 low-dimension embeddings across 33 dimensionality reduction methods and 55 scRNA-seq datasets. We employ a simple, yet novel, approach, which does not rely on the results of downstream analyses. Internal validation measures (IVMs), traditionally used as an unsupervised method to assess clustering performance, are repurposed to measure how well-formed biological clusters are after dimensionality reduction. Performance was further evaluated over nearly 200 000 000 iterations of DBSCAN, a density-based clustering algorithm, showing that hyperparameter optimization using IVMs as the objective function leads to near-optimal clustering. Methods were also assessed on the extent to which they preserve the global structure of the data, and on their computational memory and time requirements across a large range of sample sizes. Our comprehensive benchmarking analysis provides a valuable resource for researchers and aims to guide best practice for dimensionality reduction in scRNA-seq analyses, and we highlight Latent Dirichlet Allocation and Potential of Heat-diffusion for Affinity-based Transition Embedding as high-performing algorithms.

NeuroCirc: an integrative resource of circular RNA expression in the human brain.

MOTIVATION: CircRNAs are covalently closed RNA molecules that are particularly abundant in the brain. While circRNA expression data from the human brain is rapidly accumulating, integration of large-scale datasets remains challenging and time-consuming, and consequently an integrative view of circRNA expression in the human brain is currently lacking. RESULTS: NeuroCirc is a web-based resource that allows interactive exploration of multiple types of circRNA data from the human brain, including large-scale expression datasets, circQTL data and circRNA expression across neuronal differentiation and cellular maturation time-courses. NeuroCirc also allows users to upload their own circRNA expression data and explore it in the integrative platform, thereby supporting circRNA prioritization for experimental validation and functional studies. AVAILABILITY AND IMPLEMENTATION: NeuroCirc is freely available at: https://voineagulab.github.io/NeuroCirc/. The source code and user documentation are available at: https://github.com/Voineagulab/NeuroCirc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomic signature of early life stress in male rat prefrontal cortex.

Early life stress (ELS) is associated with adverse mental health outcomes including anxiety, depression and addiction-like behaviours. While ELS is known to affect the developing brain, leading to increased stress responsiveness and increased glucocorticoid levels, the molecular mechanisms underlying the detrimental effects of ELS remain incompletely characterised. Rodent models have been instrumental in beginning to uncover the molecular and cellular underpinnings of ELS. Limited nesting (LN), an ELS behavioural paradigm with significant improvements over maternal separation, mimics human maternal neglect. We have previously shown that LN leads to an increase in one of the behavioural measures of anxiety like-behaviours in rats (percent of entries in the EPM open arm). Here we assessed gene expression changes induced by ELS in rat prefrontal cortex by RNA-sequencing. We show that LN leads primarily to transcriptional repression and identify a molecular signature of LN in rat PFC that is observed across ELS protocols and replicable across rodent species (mouse and rat).

Circular RNAs: The Brain Transcriptome Comes Full Circle.

Circular RNAs (circRNAs) are a class of RNA molecules with a covalently closed loop structure formed by back-splicing of exon-exon junctions. The detection of circRNAs across many eukaryotic species, often with cell-type- and tissue-type-specific expression, has catalyzed a growing interest in understanding circRNA biogenesis and their potential functions. circRNAs are enriched in the brain, and accumulate upon neuronal differentiation and depolarization, suggesting that these RNAs are an integral component of the brain transcriptome, and may play functional roles. Here, we give an overview of the current understanding of circRNA biogenesis and function, discuss how circRNAs contribute to transcriptome complexity in the brain, and discuss recent data on the functional roles of circRNAs in the brain. We also discuss emerging data on the role of circRNAs in brain disorders and address common challenges of circRNA quantification in postmortem human brain.

TDAview: an online visualization tool for topological data analysis.

SUMMARY: TDAview is an online tool for topological data analysis (TDA) and visualization. It implements the Mapper algorithm for TDA and provides extensive graph visualization options. TDAview is a user-friendly tool that allows biologists and clinicians without programming knowledge to harness the power of TDA. TDAview supports an analysis and visualization mode in which a Mapper graph is constructed based on user-specified parameters, followed by graph visualization. It can also be used in a visualization only mode in which TDAview is used for visualizing the data properties of a Mapper graph generated using other open-source software. The graph visualization options allow data exploration by graphical display of metadata variable values for nodes and edges, as well as the generation of publishable figures. TDAview can handle large datasets, with tens of thousands of data points, and thus has a wide range of applications for high-dimensional data, including the construction of topology-based gene co-expression networks. AVAILABILITY AND IMPLEMENTATION: TDAview is a free online tool available at https://voineagulab.github.io/TDAview/. The source code, usage documentation and example data are available at TDAview GitHub repository: https://github.com/Voineagulab/TDAview.

The Landscape of Circular RNA Expression in the Human Brain.

BACKGROUND: Circular RNAs (circRNAs) are enriched in the mammalian brain and upregulated in response to neuronal differentiation and depolarization. These RNA molecules, formed by noncanonical back-splicing, have both regulatory and translational potential. METHODS: Here, we carried out an extensive characterization of circRNA expression in the human brain, in nearly 200 human brain samples, from both healthy controls and autism cases. RESULTS: We identified hundreds of novel circRNAs and demonstrated that circRNAs are not expressed stochastically, but rather as major isoforms. We characterized interindividual variability of circRNA expression in the human brain and showed that interindividual variability is less pronounced than variability between the cerebral cortex and cerebellum. Finally, we identified a circRNA coexpression module upregulated in autism samples, thereby adding another layer of complexity to the transcriptome changes observed in the autism brain. CONCLUSIONS: These data provide a comprehensive catalog of circRNAs, as well as a deeper insight into their expression in the human brain, and are available as a free resource in browsable format (http://www.voineagulab.unsw.edu.au/circ_rna).

Persistent homology analysis of brain transcriptome data in autism.

Persistent homology methods have found applications in the analysis of multiple types of biological data, particularly imaging data or data with a spatial and/or temporal component. However, few studies have assessed the use of persistent homology for the analysis of gene expression data. Here we apply persistent homology methods to investigate the global properties of gene expression in post-mortem brain tissue (cerebral cortex) of individuals with autism spectrum disorders (ASD) and matched controls. We observe a significant difference in the geometry of inter-sample relationships between autism and healthy controls as measured by the sum of the death times of zero-dimensional components and the Euler characteristic. This observation is replicated across two distinct datasets, and we interpret it as evidence for an increased heterogeneity of gene expression in autism. We also assessed the topology of gene-level point clouds and did not observe significant differences between ASD and control transcriptomes, suggesting that the overall transcriptome organization is similar in ASD and healthy cerebral cortex. Overall, our study provides a novel framework for persistent homology analyses of gene expression data for genetically complex disorders.

Interplay of LIS1 and MeCP2: Interactions and Implications With the Neurodevelopmental Disorders Lissencephaly and Rett Syndrome.

LIS1 is the main causative gene for lissencephaly, while MeCP2 is the main causative gene for Rett syndrome, both of which are neurodevelopmental diseases. Here we report nuclear functions for LIS1 and identify previously unrecognized physical and genetic interactions between the products of these two genes in the cell nucleus, that has implications on MeCP2 organization, neuronal gene expression and mouse behavior. Reduced LIS1 levels affect the association of MeCP2 with chromatin. Transcriptome analysis of primary cortical neurons derived from wild type, Lis1±, MeCP2-/y, or double mutants mice revealed a large overlap in the differentially expressed (DE) genes between the various mutants. Overall, our findings provide insights on molecular mechanisms involved in the neurodevelopmental disorders lissencephaly and Rett syndrome caused by dysfunction of LIS1 and MeCP2, respectively.

Topoisomerase I inhibition leads to length-dependent gene expression changes in human primary astrocytes.

Topoisomerase I is required for the proper expression of long genes (> 100 kb) in mouse and human cortical neurons, including many candidate genes for autism spectrum disorder (ASD) [1]. Given the important role of astrocytes in brain development [2], we investigated whether long genes, including autism susceptibility genes, also require topoisomerase I expression in human primary astrocytes. We carried genome-wide expression profiling of cultured human primary astrocytes following treatment with the topoisomerase I inhibitor Topotecan, using Illumina microarrays. We identified several thousands of differentially expressed genes and confirmed that topoisomerase I inhibition affects gene expression in human primary astrocytes in a length-dependent manner. We also identified over 20 ASD-associated genes that show topoisomerase-dependent gene expression in human primary astrocytes but have not been previously reported as topoisomerase-I-dependent in neurons. The microarray data have been deposited in NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE90052.

Transcriptional response to mitochondrial protease IMMP2L knockdown in human primary astrocytes.

IMMP2L encodes the inner membrane peptidase subunit 2, a mitochondrial protease involved in cleaving the space-sorting signals of mitochondrial membrane proteins. IMMP2L has been implicated in Tourette syndrome, but how its dysfunction contributes to the neurodevelopmental phenotype remains unclear. Here we show that IMMP2L transcription requires Topoisomerase I in human primary astrocytes, and characterize the downstream effects of IMMP2L knockdown on gene expression. We demonstrate that IMMP2L knockdown leads to dysregulation of genes involved in central nervous system development. We also find that the transcriptional response to IMMP2L knockdown partially overlaps the one induced by mitochondrial complex III inhibition. Overall, these data bring further insight into the molecular consequences of IMMP2L dysfunction in the brain.

Chromosome conformation elucidates regulatory relationships in developing human brain.

Three-dimensional physical interactions within chromosomes dynamically regulate gene expression in a tissue-specific manner. However, the 3D organization of chromosomes during human brain development and its role in regulating gene networks dysregulated in neurodevelopmental disorders, such as autism or schizophrenia, are unknown. Here we generate high-resolution 3D maps of chromatin contacts during human corticogenesis, permitting large-scale annotation of previously uncharacterized regulatory relationships relevant to the evolution of human cognition and disease. Our analyses identify hundreds of genes that physically interact with enhancers gained on the human lineage, many of which are under purifying selection and associated with human cognitive function. We integrate chromatin contacts with non-coding variants identified in schizophrenia genome-wide association studies (GWAS), highlighting multiple candidate schizophrenia risk genes and pathways, including transcription factors involved in neurogenesis, and cholinergic signalling molecules, several of which are supported by independent expression quantitative trait loci and gene expression analyses. Genome editing in human neural progenitors suggests that one of these distal schizophrenia GWAS loci regulates FOXG1 expression, supporting its potential role as a schizophrenia risk gene. This work provides a framework for understanding the effect of non-coding regulatory elements on human brain development and the evolution of cognition, and highlights novel mechanisms underlying neuropsychiatric disorders.

Searching for convergent pathways in autism spectrum disorders: insights from human brain transcriptome studies.

Autism spectrum disorder (ASD) is one of the most heritable neuropsychiatric conditions. The complex genetic landscape of the disorder includes both common and rare variants at hundreds of genetic loci. This marked heterogeneity has thus far hampered efforts to develop genetic diagnostic panels and targeted pharmacological therapies. Here, we give an overview of the current literature on the genetic basis of ASD, and review recent human brain transcriptome studies and their role in identifying convergent pathways downstream of the heterogeneous genetic variants. We also discuss emerging evidence on the involvement of non-coding genomic regions and non-coding RNAs in ASD.

Transcriptome analysis of human brain tissue identifies reduced expression of complement complex C1Q Genes in Rett syndrome.

BACKGROUND: MECP2, the gene mutated in the majority of Rett syndrome cases, is a transcriptional regulator that can activate or repress transcription. Although the transcription regulatory function of MECP2 has been known for over a decade, it remains unclear how transcriptional dysregulation leads to the neurodevelopmental disorder. Notably, little convergence was previously observed between the genes abnormally expressed in the brain of Rett syndrome mouse models and those identified in human studies. METHODS: Here we carried out a comprehensive transcriptome analysis of human brain tissue from Rett syndrome brain using both RNA-seq and microarrays. RESULTS: We identified over two hundred differentially expressed genes, and identified the complement C1Q complex genes (C1QA, C1QB and C1QC) as a point of convergence between gene expression changes in human and mouse Rett syndrome brain. CONCLUSIONS: The results of our study support a role for alterations in the expression level of C1Q complex genes in RTT pathogenesis.