Aleister Saunders - US grants

DESCRIPTION (provided by applicant): Alzheimer's disease (AD) is one of the greatest public health problems in the U.S., and its impact will only increase with demographic changes anticipated in the coming decades. Our growing understanding of AD genetics has been central to the explosion in knowledge of AD biology. Recent studies suggest that most of the genetic risk factors for Alzheimer's disease remain allusive. Efforts to unequivocally identify AD susceptibility genes have been very challenging due to the complex genetic nature of the disease. To facilitate AD susceptibility gene discovery, we propose to monitor an AD endophenotype. This endophenotype is the central pathobiologic event in Alzheimer's disease, the regulated intramembrane proteolysis of the beta-amyloid precursor protein (APP) to generate the amyloidogenic beta-amyloid (Abeta) peptide and the APP intracellular domain (AICD). The AICD fragment of APP displays transcriptional activation properties and we will harness this function to monitor APP processing. Genes that modulate APP processing should be considered functional candidate genes and thoroughly investigated biochemically and in association analyses. We are applying for funding to locate genes that modulate APP processing. To identify the genes that modulate APP processing, we propose a general experimental strategy combining existing technologies in a novel manner. Specifically, we will use RNA interference (RNAi) to silence gene expression and monitor changes in APP processing using AICD-mediated reporter gene expression. The genes to be silenced are biologically compelling and plausible positional candidate genes that reside in the two highest priority linkage regions described in our recently complete genome screen for Alzheimer's disease (Blacker et al., 2003). Genes that modulate APP processing may be genetic risk factors for Alzheimer's disease and should be considered high priority AD candidate genes.

[unreadable] DESCRIPTION (provided by applicant): This proposal is aimed at identifying and characterizing genes that regulate beta-amyloid precursor protein (APP) metabolism. Abnormal APP metabolism is a central event in Alzheimer's disease (AD). Specifically, proteolytic processing of APP to generate the amyloidogenic Beta-amyloid (ABeta) peptide is a fundamental process in AD pathogenesis. How this APP metabolism is regulated is not fully understood and a complete understanding could yield promising therapeutic avenues and insight into the genetic basis of AD. To identify regulators of APP metabolism, in the first aim we describe a focused genetic screen that combines monitoring some of the products of APP metabolism and RNAi-mediated silencing of specific human genes in a human neuronal cell line. We have previously demonstrated the successful development and validation of this approach and in this resubmission demonstrate successful identification of novel APP metabolism regulators utilizing this novel approach. In the second aim, we describe experiments aimed at determining if putative regulators modulate APP metabolism in a Drosophila model of AD. In this resubmission, we demonstrate using this model that our novel regulators do indeed regulate APP metabolism in vivo. In the third aim we describe how putative regulatory genes will be characterized in cell lines and primary neurons to determine the mechanism of APP metabolism regulation. Finally in the forth aim, we propose to determine the effects the regulatory gene on AD pathology using a transgenic mouse model of AD. Identified regulatory genes may be genetic risk factors for AD and should be considered high priority AD susceptibility genes because they regulate APP processing. Indeed, with our collaborators we have demonstrated that one of the novel regulators we recently identified contains polymorphisms that significantly increase AD risk. Our successful identification of novel APP metabolism regulators and our planned characterization of the mechanism of regulation is important to increasing our understanding of AD pathogenesis and may illuminate new avenues for therapeutic intervention. PUBLIC HEALTH RELEVANCE: Abnormal accumulation of a small protein in the brain's memory center seems to be the cause of Alzheimer's disease. Here we propose to identify the defects that are responsible for this abnormal accumulation. If we can accomplish this we may find new ways to treat Alzheimer's disease. [unreadable] [unreadable] [unreadable]

Networks are a natural, powerful, and versatile tool representing the structure of complex systems and have been widely used in many disciplines, ranging from sociology to physics to biology. Massive amounts of biological data from multiple sources await interpretation. This calls for formal information integration, modeling and mining methods. The functioning of complex biological systems demands the intricate coordination of various cellular processes and their participating components. As biological networks grow in size and complexity, the model of a biological network must become more rigorous to keep track of all the components and their interactions, and in general this presents the need for new methods and technologies in information acquisition, transmission and processing.

This research develops a set of novel methods and algorithms of integrating, analyzing and mining biological networks that include multiple sources of biomolecular information such as gene and protein expressions, interactions, and regulations, the formation and dissolution of protein complexes, and interactions between proteins and small molecules. Quantitative and qualitative bio-data from multiple sources are iteratively integrated, mined and organized to generate scalable hypothetical biomolecular network structures. The dynamics of these computational hypotheses are tested and refined through dynamic simulation and laboratory experiments. The investigators will develop an integrated modeling and mining method for the biomolecular networks governing cellular processes, with natural connections to both the biological knowledge base and the design and analysis of biological experiments. We will also plan to investigate novel graph-based theoretical models and algorithms for biological data represented as networks or graphs.