Margit Burmeister - US grants

Neurological diseases are among the most common inherited diseases in humans. The primary cause of most neurological inherited diseases is not known, but many neurological mutations in mouse have been shown to have an equivalent human counterpart. Since mice are easier to analyze and can be bred at will, most murine neurological mutations have already been mapped to chromosomes. A molecular cloning of such murine mutations will be important to understand the basic functioning of the brain, and may help in defining the cause of some human neurological diseases. We propose here to characterize and clone the genes that when mutated cause the murine neurological mutations jittery and mocha. Mocha mice are characterized by inner ear defects, reduced bleeding times and a coat color defect, Notably, they sometimes show spike-wave discharges coupled to behavioral inactivity suggestive of absence seizures. Mocha homozygous mice also show a rhythmical, synchronous, high voltage activity on EEG, which is unique to this mutant and believed to be one of the first brain rhythm traits known to arise from a single gene defect. Jittery mice are characterized by progressively worsening dystonia and ataxia with tonic- clonic seizures, and die within one month after birth. These murine mutants map genetically close to each other and are located in a region of murine chromosome 10 that is homologous to a well- characterized region of human chromosome 21. The gene for Baltic progressive myoclonus epilepsy (Unverricht-Lundborg) maps to this region. This genetic proximity suggests that jittery may be a genetic murine model for Baltic progressive myoclonus epilepsy. We will approach mapping and ultimately cloning the genes involved in these two mutants by classical genetic methods, i.e. an intraspecific backcross with Mus musculus casjaneus, and molecular genetic techniques that take advantage of the large number of tools that have been developed by us and others for the study of human chromosome 21. Isolation of these genes will allow us to study and understand the primary cause of these specific neurological mutations, increase our understanding of brain function in general, and determine whether jittery is a murine model for Baltic progressive myoclonus epilepsy.

gene mutation; genetic mapping; genetic disorder; myoclonus epilepsy; human genetic material tag;

DESCRIPTION: (Adapted from investigator's abstract) The P.I. has recently identified the mutant gene in mocha mice as the delta subunit of the adapter-related complex AP-3. They have also found that the ZnT-3 transporter is not transported correctly to synaptic vesicles, resulting in a lack of zinc in cortex and hippocampus. In this application, the P.I. proposes to genetically map all subunits of the AP-3 complex to determine if other mutants with a similar phenotype are caused by mutations in these AP-3 subunit genes, and test for interaction between mocha locus and pale-ear, the mouse homologue of HPS, the gene most commonly mutated in human Hermansky-Pudlak syndrome (HPS). They will focus on the neurological phenotype of mocha, and determine if mocha may be a mouse model for epilepsy, ADHD, autism or other neurological disorders. Mocha mutant mice have an HPS-like phenotype as well as neurological deficits (seizures, hyperactivity, spike-wave discharges, a hypersynchronized electrocortigram, increased auditory gating). In contrast, pearl mice have HPS but none of these neurological phenotypes, which they postulate is because pearl mice miss the non-neuronal form of the beta subunit, Ap3b1, but not the neuronal form of AP-3 beta, Ap3b2, whereas the delta subunit mutated in mocha is ubiquitously expressed. Dr. Burmeister postulates that inactivation of the neuronal form of AP-3 beta will result in a mouse with the neurological defects of mocha without the HPS-like phenotypes and higher fertility and viability than mocha mice. They will prepare a LoxP construct to knock out Ap3b2 in such a way that they can not only generate a complete knockout in ES cells, but also, by mating to mice in which Cre is under region-specific promoters, mice in which the AP-3 complex is missing only in specific brain regions. The P.I. will characterize the behavior of mocha, mh-2J, ZnT-3 deficient mice as well as the proposed knockout mice for the nature of hyperactivity (is it generally more active, has increased startle, or stereotypic behavior), seizure propensity, anxiety, learning and memory and electrophysiological parameters. To determine if AP3B2 plays a role in human neurological disorders, Dr. Burmeister will isolate and characterize the human AP3B2 gene and search for mutations or polymorphisms that may be present in the normal population or in patients. Given the mocha phenotype, it is anticipated that this gene may be involved in human neurological disorders characterized by increased seizure frequency and hyperactivity (e.g. autism, OCD, ADHD and epilepsy). Such polymorphisms will be made available for the scientific community to test as a candidate gene for other neurological or psychiatric disorders if justified by the results of the behavior tests.

DESCRIPTION (provided by applicant): Identifying genetic variants underlying the risk for complex disorders such as depression is extremely difficult. To complement linkage and association studies of depression, we propose a genetic association study of Neuroticism. The genetics of Neuroticism overlaps significantly with that of depression, but it constitutes a quantitative trait that can be measured in a population sample. Neuroticism is measured with the psychometrically sound, validated NEO personality inventory. Our sample consists of about 1500 subjects in about 500 families ascertained through a hypertensive proband but unselected otherwise, and about 200 hypotensive controls. More than half of the subjects have also been administered the NEO-PI, and all have given blood for genetic DMA analysis. With the first 470 subjects, we found two genetic variants associated with Neuroticism, which were replicated by other laboratories and also found associated with affective disorder in other studies. These findings confirm our underlying hypothesis that identification of genetic variants affecting Neuroticism scores will be relevant for affective disorders. In addition, a whole genome scan has been performed on our sample and will be analyzed for linkage to personality traits, including Neuroticism. In this pilot grant, we propose to continue candidate gene analysis. Candidate genes will be informed by our preliminary data, ongoing microarray analyses, the literature, and linkage analyses. We will start with candidate genes involved in the serotonin system due to our preliminary data. SNPs in these candidate genes will be either known functional (coding or promoter) variants, or selected from the HAPMAP. DNA analysis will be performed with a novel, multiplex (15-30 SNPs) genotyping system and will be compared to commercial genotyping methods for optimization and error rate determination. The nuclear family structure allows both population-based and family-based association studies to be carried out. A variance component model-based association test called QTDT, which is able to account for familial resemblance, is an ideal tool for this type of analysis Haplotype analyses are reported to be more informative than single marker analysis in association studies, and we will use generalized linear models to estimate haplotype effects. At the end, we will compare the results obtained from different association tests, and follow up on the most interesting signals. It is anticipated that this R21 will lead to a more thorough, genome-wide genetic study of Neuroticism in this valuable sample.

This Integrative Graduate Education and Research Traineeship (IGERT) award supports a new graduate training program in open data sharing and data reuse in e-science at the University of Michigan. The purpose of this program is to generate new policies, practices, and technologies for data sharing that will accelerate the pace of scientific discovery and the transfer of scientific knowledge into useful products and technologies. It will build on computer science and information science research that identifies basic principles for acquiring, managing, sharing, and archiving data and for developing new tools and technologies to put these principles into practice in bioinformatics and materials research. The program will train a cohort of doctoral students in a new way of thinking about open data. These students will be capable of contributing high quality data to repositories and effectively leveraging large volumes of data in scientific research. Students from Michigan's doctoral programs in Bioinformatics, Materials Science, Chemical Engineering, Electrical Engineering and Computer Science, and Information will be selected as IGERT trainees. The trainees will participate in a new graduate course in data curation, a multi-disciplinary public seminar series with expert speakers, and short workshops. Program faculty will also develop a supplemental summer program for undergraduates to attract diverse students and strengthen the program's impact in other scientific fields. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

DESCRIPTION (provided by applicant): Integrating large-scale genomics data has huge potential to accelerate the identification of disease genes in human. Three major challenges lie in the current integrative approach for predicting disease genes. First, previous integrations in general limit genomic data input to one species at a time, while disease datasets are often generated in multiple model organisms. Second, public functional genomic datasets are dominated and biased by certain data types and accessible tissues, which can be addressed by expert curation of input datasets. Third, when multiple tissue-specific networks have been generated, a mathematical formulation is lacking to prioritize among these competing networks for the specific disease under consideration. This collaborative proposal aims at addressing the above challenges by exploring a prototype of bioinformatics tools to integrate multiple relevant global and tissue-specific networks across mammalian species targeting a specific disease, here ataxia. This proposal is based on our preliminary data in developing both global and cerebellum-specific networks to prioritize ataxia associated genes, and on the two PIs' complementary expertise in genomic data integration and experimental ataxia gene confirmation. We will 1) use domain-specific and multiple species data to establish global, brain, cerebellum, related tissue, and ataxia-specific networks, and develop web tools to explore these networks; and 2) develop multiple kernel learning algorithms to weigh and integrate multiple networks to predict ataxia-associated genes. Although the algorithms will be developed targeting ataxia only, we envision that this expert-driven integrative approach will be adaptable to other disease gene identification scenarios.

? DESCRIPTION (provided by applicant): This is a proposal to renew and grow the University of Michigan Bioinformatics Training Program (U-M BITP), now in year 10, to 8 trainee slots/year for 2-3 years of pre-doctoral training, usually between years 1 and 3. BITP continues to be a part of our now well established and widely respected U-M Bioinformatics Graduate Program (BGP). The BGP (and its BITP) is an interdisciplinary graduate training program in bioinformatics and computational biology, drawing faculty from the School of Medicine, College of Engineering, College of Literature, Sciences and the Arts (LS&A; Including the Departments of Mathematics, Statistics, Chemistry, and Physics), the School of Public Health, School of Nursing, the College of Pharmacy, and the School of Information. The BGP and BITP are embedded in the U-M Center for Computational Medicine and Bioinformatics (CCMB), an interdisciplinary research and education center that provide the interdisciplinary research and training context. CCMB currently has 127 affiliate faculty members across the U-M, 48 of whom are participating in this BITP as potential primary mentors. CCMB is hosted within our University of Michigan Medical School Department of Computational Medicine and Bioinformatics (DCM&B), which currently has 14 primary faculty appointments, plus 12 affiliate faculty, and 4 research track core faculty members. All core DCM&B faculty members are eligible mentors of BITP trainees. BITP trainees have a full curriculum of Bioinformatics, Statistics, Data Science, and Biology/Biomedicine graduate courses to choose from, journal clubs, seminars, workshops, and special events. The BITP dissertation training utilizes a dual-mentor approach, which combines quantitative/computational and DBP application elements. The U-M-based tranSMART Foundation, cancer biostatistics, proteome informatics activities, NIDDK Metabolomics Center, and the CTSA Biomedical Informatics Program have all achieved national recognition, and are a natural magnet for BITP trainees. The goal of the BITP is to train students in bioinformatics and applied computational biology by engaging them in a rigorous curriculum and pre-doctoral training experience. BITP trainees engage in cutting-edge collaborative research featuring a strong driving biological problem (DBP) application, often leading BTP trainees to have a strong T1 Translational Research orientation. The BGP has graduated 55 Ph.D. trainees in bioinformatics since its first in 2006. Within this trainee cohort, 10 BTP traines have graduated to date. These graduates have launched exciting careers in industry, academics, and government. The BGP and BITP has established a Data Science Training Track, and is actively engaged in the emerging Michigan Institute for Data Science (MIDAS), which will be offering a Data Science Certificate to enhance bioinformatics training for Data Science Concentrators. The overall objective of the BTP is to provide the finest Bioinformatics Training environment and trainee experience available in the US.