Stephen J. Glatt - US grants

embryo /fetus toxicology; high performance liquid chromatography

This project is one of four being proposed as part of the UCSD Autism Center of Excellence. One major goal of this study is to identify, in postmortem brain tissue, distinct gene expression profiles of autism that can implicate risk genes for this highly heritable disorder. A second major goal is to identify, in circulating blood cells, a validated gene expression profile of autism that can be developed as a diagnostic tool to improve its identification and early treatment. Although autism is recognized as having a substantial genetic component, its biological basis remains unknown. Due to its high heritability, much research has focused on identifying candidate genes that influence the disorder;however, progress has been slow. In part, this may be attributable to the "single-marker" approach adopted in most prior efforts, since the etiologic complexity and heterogeneity of autism-spectrum disorders invariably thwart classification schemes relying on a single dimension to differentiate affected and unaffected children. To move beyond this single-marker approach, a major objective of the proposed project is to validate suspected risk genes for autism (e.g., genes in the apoptosis, neurogenesis, and Drosophila wingless homolog [wnf] pathways), but also to find new candidate genes by observing patterns of expression of the entire human transcriptome in eight distinct brain regions. The lack of etiologic understanding of autism has also precluded the development of biologically based diagnostic strategies. As such, the diagnosis relies solely on observable behaviors emerging during the first years of life. Yet, the advantages of a more efficient biologically based diagnostic tool for autism are numerous, and as such, another major objective of this study is to develop biologically based markers for autism. To accomplish these objectives, we will pursue five specific aims as follows: 1) Identify ubiquitous and region-specific disruptions in brain gene expression in autism;2) Identify blood-based predictive biomarkers of early-onset autism;3) Identify blood-based predictive biomarkers of autism treatment response;4) Prioritize and verify the differential expression of top candidate genes in postmortem brain and peripheral blood;and 5) Integrate the results of this project with other projects within the Center. The attainment of the :specific aims outlined above will serve to validate several groups of risk genes for autism, identify a new set of potential risk genes, and validate peripheral blood-based biomarkers of the disorder, all while determining the specificity of these effects relative to other developmental disorders and to normal development. The identification of risk genes for autism should facilitate the development of novel therapeutics, while the eventual development of a biological marker system for autism would greatly enhance the efficiency of current diagnostic methods, and it likely would facilitate the search for additional etiologic factors in the disorder.

[unreadable] DESCRIPTION (provided by applicant): This proposal responds to Request for Applications RFA-MH-08-131, which seeks Collaborative R01 applications that propose to enrich pre-existing resources for schizophrenia in the NIMH Human Genetics Initiative and to apply genomic methods to further our understanding of the molecular etiology of the disorder. The overarching aims of this proposal are to quickly and cost-effectively ascertain a large sample of trio families affected by schizophrenia, and to discover causal variants for the disorder in the first family-based genome-wide association study (GWAS) of the illness. In our recently completed NIMH-funded Genetic Linkage Study of Schizophrenia (R01MH059624; PI: Ming T. Tsuang), we established a large and efficient ascertainment network and infrastructure in Taiwan, which will again be utilized and expanded in the proposed study. Through additional ascertainment within this framework, we will collect an aggregate sample of 5,000 trios with adequate power for detecting in a GWAS those variants that make even small contributions to the risk for the disorder. We will meet the overarching goals of this project by accomplishing several Specific Aims, as follows: 1) Supplement our previously collected sample of 1,200 Han Chinese schizophrenia-affected nuclear families from Taiwan by rapidly screening and collecting an additional 3,800 trios from ten ascertainment sites in Taiwan; 2) Assess the association of schizophrenia with a genome-wide panel of single-nucleotide polymorphisms and their constituent haplotypes; 3) Perform a genome-wide survey for copy-number variations related to schizophrenia; 4) Test for gene-gene interactions (epistasis); 5) Test for gene-environment interactions, such as the well-established effect of season of birth; 6) Analyze quantitative schizophrenia phenotypes, such as symptom scores and age at onset; and 7) Enhance the NIMH Genetics Initiative collections by sending all clinical data, biomaterials, and genotypes to the appropriate repositories. The project would achieve the goals of the RFA by enriching the existing resources of the NIMH Human Genetics Initiative and by applying the latest genomic research methods to further our understanding of the molecular etiology of the disorder. Also, by capitalizing on an existing clinical infrastructure and an efficient screening and assessment protocol, we will obtain a well-powered sample in a very rapid and cost-effective manner. [unreadable] [unreadable] [unreadable]

The NIMH Research Domain Criteria (RDoC) project is intended to further a long-range goal of contributing to diagnostic systems as informed by research on genetics, neuroscience, and behavior. The Request for Applications to which we are responding encourages applications to study RDoC Constructs that cut across traditional diagnostic categories. Because RDoC Constructs are theoretical entities instantiated by behavioral and neurobiologic assessments, their validation (in the absence of a known gold standard) requires an empirical framework. This proposal seeks to apply such a framework to RDoC Constructs of the Positive Valence Systems. We will apply an updated version of the validation system proposed by Robins and Guze, which has been used for many decades as a tool for validating psychiatric constructs. We will focus on four of the five questions asked by this method: Is the Construct coherent? Is it familial? Is it associated with neurobiologic measures? Is it stable over time? Our work will answer the first three questions and will set the stage for a longitudinal study to answer the fourth. In the RDoC spirit, we will take an agnostic approach regarding nosology by ascertaining families having a child with any psychiatric disorder through referrals to our general child psychiatry clinic. We will also sample non-psychiatric comparison subjects from the community to assure that we have a wide range of scores on the RDoC Constructs represented in our study and to assess the clinical significance of Construct measures. We are adopting a clinic-based approach with appropriately matched community controls because, although the RDoC Domains are not intended to define particular disorders, any study of these domains should be more powerful when studying a sample enriched for individuals with extreme values on these traits (i.e., those with or without psychiatric disorders).2 As a consequence, we can simultaneously evaluate RDoC constructs and the predominant traditionally defined childhood psychiatric disorders in one study. Our approach is also clearly relevant to the target population of NIMH and the RDoC initiative; i.e., children and adults exhibiting impairing psychiatric symptoms. Using this sample, we will accomplish the following Specific Aims: 1) Determine if Constructs in the Positive Valence System Domains proposed by the NIMH Working Groups are homogenous theoretical Constructs; 2) Determine if Positive Valence System Constructs are familial; 3) Determine if Positive Valence System Constructs predict psychopathology and impairment; 4) Determine if Positive Valence System Constructs are associated with Construct candidate genes, with recently identified genome-wide significant cross-disorder candidate genes, and with cross-disorder polygenic scores; and 5) Establish a database of enrolled families and maintain annual contact with them to ensure the viability of longitudinal follow-up analyses.

Both the NIH and the NIA-Alzheimer's Association have emphasized the importance of early identification beginning in midlife to predict Alzheimer's disease (AD) and cognitive decline. Two keys to early identification are accurate detection of mild cognitive impairment (MCI), which can be a precursor of AD, and identification of biomarkers of MCI risk with potential for screening large populations. We have shown that blood-based transcriptomic signatures accompany and, in some cases, predict the development of some psychiatric illnesses, and others have recently found the same for MCI and AD. However, existing cross-sectional case- control biomarker studies of MCI were not designed to illuminate whether peripheral blood transcriptome biomarkers are precursors, concomitants, or consequences of MCI, and they are unable to shed light on the relative influence of inherited and environmental factors on each component of the putative biomarker signature. This is important because MCI and AD are both partially heritable disorders. Our proposed project would address these pressing questions within the context of an ongoing study that was explicitly designed to allow such inferences: our longitudinal Vietnam Era Twin Study of Aging (VETSA). The VETSA, just beginning wave 3 of longitudinal data collection from 1151 twins, began studying subjects at an average age of 56 (range: 51-60). The mean age of subjects in VETSA 3 will be 67; thus it will provide data both before and during the key transition from midlife to early old age. A large twin sample with a narrow age range that has been longitudinally characterized for many years on multiple domains (cognitive, physiological, psychological, biomedical, and genetic) makes VETSA uniquely well suited to characterizing individual differences in cognitive aging with a focus on MCI, beginning in midlife. The present proposal seeks to expand our ability to detect MCI early in VETSA subjects in a highly efficient and cost-effective manner by integrating transcriptome measurements from peripheral blood cells into the existing VETSA 3 protocol. We propose to collect an additional blood sample from all subjects in VETSA 3, extract and sequence RNA from those samples, and merge these transcriptome measures with the other data collected in VETSA to pursue three Specific Aims as part of a new VETSA Gene Expression (VETSA-GEX) project: 1) Construct an atlas of genetic and environmental influences in expression levels of all RNA transcripts (including both coding mRNAs and short and long non-coding RNAs) and gene co-expression networks detected in peripheral blood at midlife; 2) Discover, replicate, and functionally characterize peripheral blood transcriptomic signatures of neuropsychologically defined MCI and MCI severity, both by non-twin analysis of the entire sample and by co- twin-control analysis; and 3) Integrate peripheral blood transcriptome measures with other putative MCI biomarkers already being measured on these twins, including plasma ?-amyloid (A?) and phosphorylated-tau (p-tau) levels, genetic risk scores, and psychophysiological measures such as task-evoked pupil dilation.

Project Summary Over the last decade, scientists have accelerated their efforts to understand Alzheimer?s disease (AD). This has led to unprecedented knowledge of the genetic and biological bases of AD risk, and vast stores of valuable data for further mining. Understanding the genetic and biological risk states for AD is, in itself, extraordinarily valuable for guiding mechanistic studies, developing better diagnostics, and formulating therapeutics. But an understanding of risk states also has the benefit of allowing research on resilience to AD. Research on the genetic and biological bases of resilience necessarily lags behind the discovery of risk factors. Now, as the risk architecture of AD is coming into view, it is feasible to study resilience to AD in individuals who are cognitively normal despite being at elevated risk for the disease. The approach we have devised for identifying resilience factors is straightforward yet, to our knowledge, unprecedented. We identify unaffected individuals at the highest levels of multivariate risk, match them to affected individuals at equivalent levels of risk, and contrast these two subgroups to find residual variation associated with the absence of disease. In this project, we will capitalize on the wealth of existing high-throughput AD risk-factor results and data, and our involvement in many of the world?s largest AD consortia, to efficiently map resilience to AD at three levels (genetics, transcriptomics, and neuroimaging), and to integrate across these levels. In Aim 1, we will identify genetic variation associated with resilience to AD in the presence of elevated genetic risk conferred by APOE ?4 alleles, an elevated AD polygenic risk score, or an elevated AD polygenic hazard score. In Aim 2, we will mega-analyze all available transcriptomic data from studies of postmortem hippocampal tissue and of peripheral blood in AD to identify transcriptomic risk scores and machine-learning algorithms that maximally distinguish AD from cognitively normal control subjects, and scores and algorithms that then identify residual transcriptomic variation that offsets the transcriptomic risk in resilient controls. In Aim 3, we will identify an MRI-based structural brain signature that is associated with resilience to AD in the presence of an AD- associated cortical risk signature. Lastly, in our exploratory Aim 4, we will integrate genetic, transcriptomic, brain structural, and clinical data to identify biological relationships across Aims, and novel phenotypes of resilience. Collectively, these Aims will identify multivariate, genetic, transcriptomic, and brain-structural profiles of resilience to AD, as well as molecular, neurobiological, and clinical phenotypes stemming from AD- resilience genotypes.

Abstract The pathophysiology of brain disorders remains unknown because we cannot study the relevant tissue in living human subjects. Postmortem brain tissue is useful, but expensive, rare, and critically confounded by antemortem and agonal factors. These two facts have inspired the search for alternative strategies, such as the use of surrogate markers like blood-based gene expression. To improve the rigor of this widely used approach, we developed a novel computational method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that leverages biological comparability between blood and brain gene expression to predict transcriptome profiles for brain tissue based on blood gene-expression profiles. BrainGENIE is fundamentally different from other transcriptome-imputation methods, and captures a much larger proportion of the variance in?and larger fraction of?the brain transcriptome. BrainGENIE is capable of predicting approximately 9?57% of the brain transcriptome, which yields an approximate 1.8-fold increase in coverage relative to the ?gold standard? method PrediXcan, and which greatly improves our statistical power to detect genes and pathways associated with disease. Our proposal contains three Specific Aims to improve our method and shed light on biological pathways underlying neuropsychiatric disorders. Aim 1: Refine BrainGENIE to capture additional genes that are not currently well predicted by our method. Aim 2: Apply BrainGENIE to our collection of publicly available and in-house data to predict brain-region-specific gene expression profiles for over 8,000 living persons, and discover region-specific gene-expression patterns associated with neuropsychiatric disorders and neurodegenerative diseases. Aim 3: Disseminate BrainGENIE as stand-alone software for other researchers to use freely. Guided by recent genetic and genomic studies, we hypothesize that comparable patterns of gene dysregulation will be found across neuropsychiatric disorders among pathways involving innate immunity, chromatin remodeling, neurodevelopment, and neurotransmission. Inclusion of neurodegenerative disorders in our analysis will allow us to determine whether gene expression patterns are shared across a broader range of brain disorders. We also expect to identify disorder-specific and brain-region-specific transcriptomic associations. Our project will enable new lines of inquiry into biological changes that emerge in the brains of living persons, and create opportunities to improve diagnostics, intervention, and treatment.