Jason L. Stein, Ph.D. - US grants

DESCRIPTION (provided by applicant): Schizophrenia is a devastating illness, known to be genetically mediated;however, no gene has been significantly associated with the illness at genome wide significance levels in published meta-analyses. Schizophrenia is also known to be a disconnection syndrome, whereby abnormal white matter tracts in the brain are thought to contribute to the development of the illness. We seek to elucidate the genetic basis of schizophrenia by studying variations in white matter tracts found to be involved in schizohprenia. In order to study the white matter tracts, we will use diffusion weighted imaging in a large sample of healthy monozygotic and dizygotic twins (N=1150) with detailed genome wide genotype data to study the relation between genetic polymorphisms and tract integrity and geometry. This unique population will allow us to determine the heritability, or the degree of genetic mediation, of these tracts of interest. For those tracts which have properties that are highly heritable, we will be able to associate genetic polymorphisms of these individuals with the tracts of interest. We will then have identified genes involved with variations of tract integrity and geometry which then will also be associated with schizophrenia. This will be the first study to take an imaging genomics approach for identifying risk genes: we will survey the entire genome for statistical association to a brain trait of interest. This study will also help provide a biological mechanism for schizophrenia, help discover novel gene targets for therapeutics, provide a more accurate and biologically based diagnostic criterion for the disorder, and provide a method to discover the genetic basis of other mental illnesses that can be generally adopted by the biotechnology community. Public Health Relevance: To identify genes associated with this schizophrenia, we will study a known deficit of the illness: aberrant connections between different brain regions. Using a new technique of brain imaging capable of studying these connections in the living brain, and by studying the entire genome of many healthy individuals, we will be able to find variations in connections that are associated with alterations in the genome. By following this plan, we expect to find novel genes involved in the development of schizophrenia, help to provide a biological mechanism for schizophrenia, help discover targets for therapeutic medications, and provide a more accurate and biologically based diagnostic criterion for the disorder.

DESCRIPTION (provided by applicant): Neuropsychiatric diseases like schizophrenia and autism have debilitating consequences to individuals and skyrocketing costs of care, creating a great burden to patients and society. The current state of treatment remains plagued by poor outcomes. Genetically mediated abnormalities in the development and architecture of the cerebral cortex play an important role in these diseases. Human neural progenitor cells can be used to model aspects of cortical development in a dish, permitting high-throughput study of disease-associated phenotypes. Here, we will study the genetic underpinnings of two key features of human cortical development: (1) longitudinal changes in gene expression, and (2) neuronal morphology and synaptogenesis. Utilizing the inherent genetic variation observed in neural progenitors from approximately 150 donor lines, we will conduct longitudinal genome-wide association studies to identify the specific genetic variants governing quantitative aspects of human cortical development. This will constitute the first systematic identification of genetic loci influencing cellular neurodevelopmental phenotypes in human cells. We will bridge the gap between genetic variants, molecular and cellular biology, and disease states using model systems of cortical development. Once neurodevelopmental quantitative trait loci are identified, we will then search for a mechanism by which genetic variation causes phenotypic change using recently available genomic engineering techniques. Employing bioinformatics and molecular cloning, we will introduce mutations which have a demonstrated effect on cortical development into the genome of human neural progenitor cells. Cell specific models of disease- related processes will open the door for the discovery, development, and rapid screening of therapeutics. To accomplish the proposed research plan, I will pursue two years of intensive training in neurodevelopmental biology, genetics, and bioinformatics under the supervision and guidance of my mentors. Daniel Geschwind, MD, PhD, my primary mentor and an expert in neurodevelopmental biology and genetics, along with Eleazar Eskin, PhD, my co-mentor and an expert in bioinformatics and statistical genetics, will instruct me in the skills necessary to successfully complete this project. Using the high-throughput sequencing, high content screening, and computational cluster resources found at UCLA, I will be able to efficiently complete these studies with the most current technology available. With the skills learned from this project, I will transition to academic faculty as an independent investigator, pursuing translational research in neuropsychiatric disorders.

Our understanding of nervous system function is critically dependent on visualizing the three-dimensional structure of the brain. The brain is composed of many cell types that are organized into complex networks to produce neural functions such as cognition. The brains of patients with neuropsychiatric illness often display disorganized cell placement as compared to brains of healthy individuals. It is therefore fundamentally important to determine how the details of cell type and organization relate to cognition and behavior. Recent advances in structural brain imaging have enabled the possibility for creating digital data sets of complete intact brain specimens at cellular resolution. However, given the enormous number of neurons in the mammalian brain, the data sets produced with these techniques are so large as to hamper the research process. In this project, we will present a novel computational infrastructure framework that addresses this challenge, with the ultimate aim of facilitating collaboration among laboratories that generate and use these large cellular imaging data sets for neuroscience discovery. This project therefore aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.

Recent innovations in tissue clearing techniques and light sheet microscopy allow the rapid acquisition of three-dimensional micron resolution images in fluorescently labeled brain samples. However, the enormous size of the resulting information-dense data sets present great computational challenges to sharing, analysis, and visualization of these data in a standardized manner across multiple laboratories. In this project, the combined expertise of three connected and complementary centers associated with the University of North Carolina at Chapel Hill will be leveraged to address this issue through development of a unified and highly scalable computational infrastructure framework that can be harnessed by the neuroscience community. The several aims are to develop cyberinfrastructure for the distributed storage, sharing and analysis of high-dimensional images; develop high throughput computational tools for quantitative analysis of 3D microscopy images; provide the means for efficient visualization of results using immersive environments; and demonstrate the utility of these tools by applying them to the analysis, sharing, and visualization of brain structure deficits in an autism mouse model.

This Early-concept Grants for Exploratory Research (EAGER) award by the CISE Division of Advanced Cyberinfrastructure is jointly supported by the CISE Division of Information and Intelligent Systems, with funds associated with the NSF Understanding the Brain, BRAIN Initiative activities, and developing national research infrastructure for neuroscience. This project also aligns with NSF objectives under the National Strategic Computing Initiative.

Project Summary A multitude of common genetic variants influencing risk for neuropsychiatric disorders (e.g., schizophrenia, major depressive disorder, and Alzheimer?s disease) have recently been identified and replicated, providing a foothold into the causes of these disorders. The critical next step in neuropsychiatric genetics is to move from a risk locus in the genome to an understanding of how this genetic variation influences molecules, cells, and circuits of the brain, leading to complex disorders. Many datasets, including those generated by our own labs, have established direct links between genotype and human brain traits at multiple levels of biology (molecular: chromatin accessibility, expression; cellular: morphology; circuit: gross brain structure), termed quantitative trait loci (QTLs). Here, we will integrate QTLs across multiple levels of biology in order to statistically prioritize causal pathways by which genetic variation creates risk for complex neuropsychiatric disorders. Causal modeling goes well beyond previous co-localization work, as it allows the prioritization of expensive functional validation experiments for cellular or molecular changes that are a cause of the disorder, rather than those that are a consequence or independent of the disorder. It additionally allows inference of key experimental parameters including cell-type and developmental time period. Finally, causal inference when combined across multiple levels of biology and multiple disorder risk loci allows for assessment of convergence at a biological level, cell-type, or developmental time period, which is critical information for therapeutic targeting. We will leverage the computational and statistical frameworks of Bayesian probabilistic networks and causal inference in a new framework that utilizes association summary statistics, as well-powered multi-level data collected on the same individuals is almost always infeasible. Subsequently, we will experimentally validate the molecular predictions of our model using epigenetic engineering in primary human neural progenitor cells, and in turn revising the computational models. Prioritizing causal molecular pathways of disorder associated variants, and identifying the relevant cell-type and developmental stage will increase the success rate of validation experiments and shed light on mechanisms of neuropsychiatric disorders in an unbiased manner.

Project Summary Many individuals with monogenic and idiopathic forms of autism spectrum disorder (ASD) exhibit larger brain volumes and a hyper-expansion of neocortical surface area early in life. An increase in the neural progenitor pool, present almost exclusively during fetal development, is a well-described mechanism that can lead to expansion of neocortical surface area. Numerous ASD-linked mutations affect genes involved in Wnt signaling, a pathway that regulates neural progenitor proliferation. In addition, common genetic variants near Wnt- pathway genes are associated with changes in cortical surface area in adults. Common variation plays a large role in ASD risk. Large and ever-growing genome-wide association consortia are identifying common variant loci associated with ASD risk. Based on these data, we hypothesize that both common and rare variants impact neocortical progenitor proliferation in fetal development by altering Wnt signaling, leading to post-natal cortical surface area hyper-expansion and increased ASD risk. The primary goal of this project is to identify common genetic variants influencing gene expression, Wnt signaling, and proliferation in response to Wnt modulators in primary human neuronal progenitor cell lines (phNPCs) from 107 genetically diverse donors. To accomplish this goal, we will first quantify inter-individual variability in transcriptional response to Wnt modulators. To modulate Wnt signaling, we will utilize two clinically relevant compounds (Valproic Acid, Lithium Chloride) as well as the most potent and selective Wnt activating compound available (CT99021). Next, we will quantify both canonical Wnt signaling, through a high-throughput luciferase assay, and proliferation, through a flow cytometry assay, in response to these Wnt modulators in this population of cells. Finally, we will perform a genetic association using the phenotypes we have collected on these cell lines to identify common variant loci associated with transcriptional, Wnt signaling and neural progenitor proliferation responses to Wnt modulators. We will determine if these same loci also influence risk for ASD and cortical surface area through co- localization techniques using existing genome-wide association studies. This study may allow us to understand the mechanism of action of ASD-associated genetic variation, implicating a cell-type and developmental process, which may lead to a more thorough understanding of ASD pathogenesis. In addition, as prenatal exposure to Valproic Acid is a known environmental risk factor for ASD, this study may identify genetic variants that impact responsiveness to Valproic Acid, allowing the prediction of adverse neurodevelopmental effects based on genotype.

Project Summary/Abstract Brain development involves the organized differentiation of neural progenitors into neurons and glia, tightly orchestrated in both temporal and spatial domains. Alterations in embryonic brain development can manifest as altered post-natal brain structure and function, leading to neuropsychiatric illness. Recent advances in tissue clearing technology and light-sheet microscopy have allowed for rapid cellular resolution image acquisition in intact whole brains. The ability to analyze these large datasets has lagged behind the ability to acquire them, resulting in their most common use as visual anecdotes rather than quantified results. In this proposal, we will develop computational tools to specifically quantify the developmental trajectories of individual cell-types in the entire brain. We will apply tissue clearing technology and light-sheet microscopy to study how development is altered in autism-associated CHD8 heterozygous mutant mice. Heterozygous CHD8 loss of function mutations result in macrocephaly in both human patients and mouse models. We will first acquire whole brain cellular resolution images of neural progenitor and neuronal cell-types across critical time- periods of neocortical neurogenesis in wild-type and Chd8+/- mice. We will then develop longitudinal image registration algorithms to map the developmental trajectories of neocortical development. Finally, we will quantify cell-type distributions within annotated areas of the developing neocortex. Completing the aims of this proposal will elucidate the cellular basis and spatial localization of brain overgrowth in autism.

The vast majority of common genetic variation underlying risk for neuropsychiatric disorders resides in poorly annotated non-coding regions of the genome and likely impacts the regulation of gene expression. In order to move from a location in the genome associated with risk to a regulatory mechanism, there are several major gaps in knowledge including: (a) the causal variant(s) within the associated locus, (b) the regulatory elements impacted by those causal variant(s), (c) the cell-type(s) and developmental time period(s) at which the causal variants(s) exert their effects, and (d) the gene(s) impacted by those causal variant(s). In this proposal, we will identify genetic influences on two features of chromatin architecture (enhancer histone marks and their 30 interactions) during human cortical development in order to more completely explain regulatory mechanisms leading to risk for neuropsychiatric disorders. In a large population of post-mortem human developing cortical tissue that has previously undergone genome-wide genotyping and transcriptomic profiling, we will utilize a technique that allows us to simultaneously measure enhancer activity and its interaction profile (H3K27ac HiChIP). We will then identify genetic influences on these two features of chromatin and their co-localization with previous and growing neuropsychiatric disorder genome-wide association (GWAS) risk loci. Psychiatric disorder risk variants may exert their regulatory impact by (1) changing enhancers (H3K27ac QTLs or histone acetylation (ha)QTLs) and/or (2) chromatin interaction (interaction-QTLs). This novel class of QTLs will enhance our understanding of the molecular processes underlying human neurodevelopment and how that development is altered in neuropsychiatric disorders. Further, we will conduct two orthogonal methods to validate the impact of the genetic variants and assess their cell-type specificity. We will perform cell-type specific massively parallel reporter assays (MPRA) to validate the functional impact of haQTLs. In this assay, cloned oligos containing the enhancer associated alleles drive expression of barcoded transcripts that can be used to assess regulatory differences and identify causal variants. We will also apply a haplotype-specific chromatin imaging technique to visualize how regulatory variation impacts chromatin interactions in individual nuclei. This technique paints sections of each chromosome with allele-specific oligos in order to visualize and measure the physical interactions of the 0NA molecule. Completing the aims of this proposal will allow us to identify largely complete regulatory mechanisms impacting human brain development and risk for neuropsychiatric disorders.

ABSTRACT Growing cognitive demands over the course of human evolution have shaped the adaptation of human brains for increasingly complex higher cognitive functions, like executive control, social cognition, attention, and language. Research on those higher cognitive functions has focused predominantly on parts of the neocortex and related subcortical areas that comprise forebrain networks linked to specific cognitive functions. Recent research makes it clear, however, that each of those forebrain networks is functionally connected to distinct regions of the cerebellum. Surprisingly, evolutionary studies show further that it is those parts of the cerebellum that show the most dramatic expansion in humans compared to non-human primates, and even in modern humans compared to Neanderthals. In humans living today, individual variation in the size or functional connectivity of those cerebellar regions has been linked to disorders affecting higher cognitive functions, such as autism spectrum disorder (ASD), attention-deficit/hyperactive disorder (ADHD), and schizophrenia. These converging results suggest strongly that molecular and cellular mechanisms controlling the development and functional organization of the human cerebellum have undergone systematic changes that have proven functionally important in modern humans. The proposed studies begin to map out those changes, beginning with a genome-wide association study (GWAS) using an existing dataset of structural MRI images of cerebellum from 30,000 genotyped human participants to identify genes and genomic variants associated with overall cerebellar volume and individual differences in relative size and gray matter thickness across different regions of the cerebellar cortex (Aim 1). A parallel study (Aim 2) will use single-cell genomics of human, macaque, and mouse cerebellum to investigate possible differences in gene expression FKURPDWLQ DFFHVVLELOLW\ and the cell type composition of intrinsic cerebellar circuits between humans and other animals (Aim 2). Together, those studies address an essential but unresolved issue, whether expansion of the cerebellum in humans represents a simple increase in capacity of a basic cerebellar circuit module that is otherwise unchanged in humans, or whether the local circuitry in expanded regions of the cerebellum has undergone functionally significant modifications. In the final part of this research (Aim 3), evolutionary analysis will identify specific regulatory elements within the genes identified in the first two aims that show accelerated rates of substitution in humans or evidence of positive, purifying, or balancing selection over the course of human evolution, and whether evolutionary selection has tended to increase or decrease diversity at these sites in since the divergence of modern humans from other primates. These studies will allow us to identify specific regulatory elements or other variants that have been targets of natural selection within the genes involved in cerebellar development or adult cerebellar functions, and to compare those targets of evolutionary selection to specific variants associated with individual variation or increased risk for major psychiatric disorders in modern human populations.