Joel Dudley - US grants

DESCRIPTION (provided by applicant): Genetic association studies have been successful in identifying >1,000 genetic loci associated with complex disease traits in human populations. However, it remains a central challenge to interpret the vast amounts of data generated by GWAS studies towards an improved understanding of disease markers and, thus, mechanisms, which are critical for translating GWAS findings into genomic medicine applications enabling improvements in diagnostics, therapies, and outcomes. Recent efforts to incorporate prior biological information into GWAS analysis has greatly enhanced the interpretation of GWAS findings by providing biological frameworks for prioritizing associations, and for interpreting multiple associated loci within the contexts of biological networks and pathways. We recently demonstrated that position-specific evolutionary priors could be incorporated into analysis of GWAS results to prioritize variants that were more reproducible across studies. We propose to develop, investigate, and apply evolutionary informed integrative methods that embrace and leverage the genetic complexity of common disease. We hypothesize that position-specific evolutionary features can be incorporated into multiscale biological pathway and network analysis, and that evolutionary informed pathway and network analysis can be applied to existing GWAS and clinical data sets to identify mechanisms giving rise to complex disease phenotypes in populations and individuals. We propose to develop and evaluate these hypotheses through pursuit of the following specific aims: (1) Develop novel evolutionary-informed pathway and network analysis method for interpreting GWAS findings. (2) Apply novel methods to established GWAS and clinical data for T2D to elucidate disease mechanisms underlying the genetic architecture across populations. (3) Develop a public database and software tool to enable evolutionary informed network analysis of GWAS findings for the broader research community.

The Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG) at Mount Sinai will assemble, organize and visualize data collected from the under-studied druggable genome from the four families: protein kinases, nuclear receptor, ion channels and GPCRs. The KMC-IDG will also attempt linking such under-studied druggable targets for their potential applications in various diseases. To achieve this we will assemble and abstract data from four domains: proteins/genes/targets, drugs/perturbagens, diseases/phenotypes/side-effects, and data from individual patients. Various pipe-lines and workflow will be established to connect clusters of patients from various diseases to under-studied druggable targets. The Ma'ayan and Dudley Labs are well positioned to carry out successfully this project based on their prior track record of productivity, foundation of source code and data that is already collected and organized, and strong existing user base that can be directed to the newly developed portal. In addition, both labs have a strong track record of collaborations including the computational identification and experimental validation of at least one under-studied protein kinase as a potential important target for attenuating kidney fibrosis. One unique and innovative research component of this project is an investigation into the sources of the literature and experimental biases that exist in the molecular and cellular biology research domains.

The Data Organization Core (DOC) of the Mount Sinai's KMC-IDG will collect, process, and maintain attributes about the druggable targets for all proposed families: protein kinases, G-protein coupled receptors, nuclear receptors and ion channels. The emphasis will be to focus on those genes/proteins that are understudied and collect unbiased genome-wide profiling datasets. In addition, the DOC will collect, process and maintain data tables and attributes for all other genes/proteins, drugs/small-molecules and other perturbagens, pheontypes/diseases/side-effects, and clinical as well as genomics datasets from cohorts of patients. This will enable us to identify links between and across genes/proteins networks, drugs/small-molecules and other perturbagens networks, pheontypes/diseases/side-effects networks, and clusters of individual patients with similar profiles. For this, the Core will develop and apply clustering and classification algorithms as well as workflows to make predictions about the potential applicability of targeting the understudied proteins for various translational applications in personalized medicine.

? DESCRIPTION (provided by applicant): The purpose of the R34 planning grant is to support the effort, conferences, site visits, and other fact-finding processes involved in designing the translational center, Systems Medicine Drug Discovery for Alzheimer's disease. This Center will include Core Facilities charged with providing key technology services and Pathway/Drug-Based Projects that will focus on specific molecular mechanisms and/or predicted pharmacological interventions. Project leaders will be responsible for moving each project (target pathway, drug) through the relevant cores with maximum efficiency. Pathways and projects will undergo continual reassessment of priorities. The goals of the planning period are as follows: (Specific Aim 1) To assemble a team of investigators with appropriate expertise and to develop the most efficient strategy for their productive communication and collaboration; (Specific Aim 2) To develop a plan for generation and exploitation of diverse genetic and omic data collected in humans and various animal models to identify translatable pharmacodynamic biomarkers and enable modeling of drug response determinants in distinct patient populations; (Specific Aim 3) To develop a plan for establishment of multi-scale computational models of pharmacological mechanism, that bridges the divide between cell-level biochemical models and organism-level PK/PD and neuroimaging models; (Specific Aim 4) To plan the most efficient strategy for target validation that will involve conducting quantitative analysis of the effects of small molecules and biologics on therapeutic targets across multiple scales of biological complexity; (Specific Aim 5) To plan the most effective strategy for investigation of the molecular and physiological origins of variability in drug response at the single-cell, organ, and patient level that arises from differences at the level of the proteome, genome and environment; (Specific Aim 6) To develop systems approaches for comprehensive and systematic failure analysis during preclinical and clinical drug development; (Specific Aim 7) To develop the most efficient strategy for rapid and broad sharing of data, analytical and research tools, and models prior to publication; (Specific Aim 8) To ensure open source data enablement and to develop strategies for removing legal/IP barriers to sharing data, biological samples and research tools; (Specific Aim 9) To develop curricula, workshops and seminars for training in systems biology, systems pharmacology, pharmacometrics, PK/PD modeling, omics technologies, translational bioinformatics and other quantitative science areas; (Specific Aim 10) To provide short term training for junior staff and short term sabbaticals for senior team members to expand existing expertise or develop new expertise essential for achieving the programmatic goals of the translational center(s) initiative. Within the planning year, the R34 investigators predict that these Aims will be achieved and that the foundation will have been laid for writing the center proposal.

The User Interface Portal (UIP) of the Mount Sinai's KMC-IDG will develop a state-of-the-art web-site that would host the presentation and access to the data collected by the DOC. The web-site will have a dedicated page of each under-studied druggable target with different plug-in tools to explore the various aspects of each target including: protein structure, protein-protein interactions, regulation by transcription factors, mouse phenotypes, expression profiles in different tissues and conditions, mouse knockout phenotypes, gene ontology information, post-translational modification and many more. In addition, the portal will enable interactive visualization of various applications that will place the under-studied targets within networks made of cohorts of patients, cell lines, diseases/side-effects/phenotypes, drugs and other genes and proteins. The UIP will have a powerful search engine that would index all entities in the DOC and will learn from user experience. The UIP will enable users to build their own data analysis pipelines based on the user specific needs. In addition, the UIP will be designed in a plug-in architecture to enable the community to contribute data analysis and visualization tools.

Previous studies associate various microbes with Alzheimer's disease (AD), but the question of whether microbe-related antigens represent a causal component of AD or are an opportunistic passenger of neurodegeneration is difficult to resolve. We propose to expand on our preliminary work mapping biological networks underlying AD-associated phenotypes using independent human post-mortem data sets. First, we apply a computational approach to build region-specific biological networks from `preclinical AD' samples measured from individuals that meet neuropathological criteria for AD, but who were cognitively intact at the time of death. Specifically, we construct regulatory networks from entorhinal cortex (EC) and hippocampal (HIP) tissue and compared the structure of preclinical AD and control networks. Network analysis identified several sub-networks and key drivers perturbed in preclinical AD, including enrichments relevant to viral susceptibility loci among network drivers, including roles for C2H2 zinc fingers, G4-quadruplex activity, and miR-155 in regulating diverse viral factors. Network analysis of a second independent data set characterizing individuals with `clinical AD' identified more direct evidence for network mechanisms of viral activity in AD. The availability of DNA and RNA sequencing allowed us to evaluate the direct presence of viral sequences and quantify association with AD. We observe increased abundance of human herpesvirus 6A (HHV-6A) and human herpesvirus 7 (HHV-7) across multiple regions from subjects with AD compared with controls. We replicated these findings in two additional, independent and geographically dispersed AD cohorts. We built preliminary network models of host- virus regulatory interactions and virus-virus interactions to correlate viral species with the severity of cognitive impairment and neuropathology. We find evidence that the virus-host landscape is shaped by both competitive and synergistic interactions between multiple viral species, with impacts on amyloid precursor protein (APP) processing, cytoskeletal organization, protein synthesis and innate immune response. We identified host DNA variants that associate with viral abundance (vQTL), and that vQTLs associate with increased AD risk, clinical dementia severity, and neuropathology severity, indicating a shared genetic basis linking risk for AD, severity of AD neuropathology, and abundance of specific viral species. We propose to perform computational and experimental work to further elucidate the specific network mechanisms causal drivers or viral pathogens in AD pathophysiology. We aim i) to map and models the roles of specific viral species in modulating pathogenic molecular, genetic, and clinical networks in preclinical and clinical AD, ii) to evaluate the roles of C2H2 zinc finger proteins and G-quadruplex sequences in mediating molecular pathology of preclinical and clinical AD, and iii) to identify and evaluate specific molecules that mediate viral effects upon the molecular pathology of preclinical and clinical AD.

PROJECT SUMMARY Nonalcoholic steatohepatitis, or NASH, is a serious and escalating health threat in the United States, affecting at least 2-5% of the population, with a rising incidence paralleling the obesity epidemic. There are no effective medical treatments. NASH is a ?silent? liver disease characterized by hepatic accumulation of fat accompanied by inflammation and hepatocellular injury (?ballooning?). NASH is a progressive disease that can culminate in cirrhosis and a heightened risk of primary liver cancer. It is the second leading cause of liver failure and will supplant hepatitis C as the primary indication for liver transplantation by 2020. The accelerating impact of NASH underscores the urgent need to develop novel therapies that prevent progression or, in advanced stages, reverses inflammation, injury and fibrosis. Drug repurposing is an attractive approach to identify novel therapeutics for NASH because it can greatly shorten the drug development timeline. To exploit new computational strategies to uncover relevant repurposed drugs we applied a chemogenomic drug repurposing algorithm in collaboration with AstraZeneca, to identify novel indications for a set of AstraZeneca compounds that previously failed human efficacy studies for various indications apart from safety concerns. Our analysis identified a specific compound that previously failed efficacy trials for a gastrointestinal indication as a drug repurposing candidate for NASH. The compound's mechanism of action would be considered quite novel for treating NASH, and NASH would represent a leap to a completely new disease area compared to the compound's original indication. In the UH2 phase of this grant we propose to perform in vitro and in vivo pre- clinical studies to evaluate the efficacy of the repurposed compound for treating NASH. We will achieve this goal through the following aims: Aim 1) Experimentally validate molecular engagement of a novel drug repurposing candidate for NASH. Aim 2) Evaluate the efficacy of a novel drug repurposing candidate for NASH using a murine model of disease. If the milestones from the UH2 phase are successfully achieved and a ?go? decision point is reached, we will use the UH3 phase of the grant to plan a phase 2a clinical trial. We have assembled a multidisciplinary team with demonstrated expertise in liver disease, drug repurposing, genomics, basic and clinical analysis of liver disease, clinical trials, and pharmaceutic drug development. The team capabilities and expertise along with the established collaboration between Mount Sinai and AstraZeneca provide a seamless path to move from pre-clinical studies directly to human clinical trials.

ABSTRACT Activation of immune pathways and networks constitutes an important emerging component of Alzheimer's disease (AD) pathology that presents new opportunities for therapeutic discovery. The overall goal of the study is to define high-quality immune activation networks for AD and to identify novel therapeutic opportunities against these networks using computational drug repurposing. We propose three distinct but complementary aims that leverage recent insights into the genetics, functional genomics, and network biology of immune factors in AD along with computational drug repurposing. First, we will define microglia-associated networks and use these as input to computational drug repurposing methods to identify novel therapeutic opportunities. We will also focus viral perturbations as an environmental source of immune activation in AD. We will build from our preliminary data that shows the influence of specific viral species in networks constructed from multi- omic measures of post-mortem brain tissues. We will then use these virally-modulated AD networks as input to computational drug repurposing to identify novel therapeutic opportunities. Finally, we will evaluate the temporal dynamics of immune activation in AD. AD is a disease of aging that manifests over a period and we hypothesize that role of immune activation in AD is likely to vary along stages of disease. We will study microglial and multi-omic networks in mice lacking each of the specified microglial genes at ages 4, 8, 12, and 24 months characterize the molecular, cellular, and histological effects of immune network activation in AD animal models across animal age and disease stage. The expected outcome of this study is to identify and evaluate drugs that could be repurposed to modulate immune networks towards non-disease states and to potentially identify the most relevant or optimal states or stages of disease for evaluating therapeutic agents targeting immune activation pathways in AD.