Alzheimer's disease (AD) affects half of the US population over the age of 85 and causes destruction of select networks and cell groups within the brain. AD manifests initially as mild cognitive decline, but gets progressively worse and is always fatal. Despite significant progress identifying susceptibility loci for AD in genome-wide association and whole exome sequencing studies, to date, a predictive risk score for AD that achieves clinical utility on an individual basis given DNA variation information alone has been elusive. This proposal aims to develop a multiscale-network approach to elucidating the complexity of AD. Multiscale network models causally linked to AD will be developed based on existing AD-related large scale molecular data and the high-impact, high-resolution complementary datasets generated through this application. Using brain slice cultures, iPS-cell-derived mixed cultures of human neuronal, oligodendroglial, and astrocytic cell systems, and fly models of AD, we seek to reconstitute the AD-related networks discovered in the multiscale analysis in these living systems and then employ high-throughput molecular and cellular screening assays to not only validate the actions of individual genes on molecular and cellular AD-associated processes, but also validate the molecular networks we implicated in the disease. Our initial multiscale studies have implicated the microglial protein TYROBP as one key driver of AD pathogenesis, a """"""""hit"""""""" we have partially validated, but that we will further validae along with other hits using iPSC-derived mixed cultures of different brain cell types, murine brain slices and AD fly models. We will analyze the potential ability for network-derived hits like TYROBP to modulate standard AD pathology involving A? and tau as well as its ability to shift networks in those same systems in such a way as to reflect the behavior of networks discovered in the multi-scale analysis. Importantly, the model building and validation will be iterated to produce updated/refined models based on validation results that, in turn, will be mined to generate updated lists of prioritized targets for validation. In this way, through the course of th grant, as new knowledge accumulates externally and as we generate increased amounts of data including validation data, our models will take into account the most up to date information to produce the most predictive models of AD. As a service to the AD research community, we will provide dramatically improved general access to large-scale, multidimensional datasets, together with systems level analyses of these datasets.

Public Health Relevance

We will develop and apply a multiscale-network approach to elucidate the complexity of Alzheimer's disease (AD) via the unbiased integration of large-scale molecular, cellular, and clinical data. Using murine brain slices, mixed human cell cultures of relevant brain cell types, and fly models of AD, we will reconstitute the AD-related networks discovered in the multi-scale analysis in these living systems and then employ high-throughput molecular and cellular screening assays to not only validate the actions of individual genes on molecular and cellular AD-associated processes, but also to validate the molecular networks predicted to drive AD.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01AG046170-02
Application #
8735843
Study Section
Special Emphasis Panel (ZAG1)
Program Officer
Petanceska, Suzana
Project Start
2013-09-20
Project End
2018-08-31
Budget Start
2014-09-15
Budget End
2015-08-31
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Icahn School of Medicine at Mount Sinai
Department
Genetics
Type
Schools of Medicine
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10029
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Moreno, Cesar L; Della Guardia, Lucio; Shnyder, Valeria et al. (2018) iPSC-derived familial Alzheimer's PSEN2 N141I cholinergic neurons exhibit mutation-dependent molecular pathology corrected by insulin signaling. Mol Neurodegener 13:33
Lee, Eunjee; Collazo-Lorduy, Ana; Castillo-Martin, Mireia et al. (2018) Identification of microR-106b as a prognostic biomarker of p53-like bladder cancers by ActMiR. Oncogene 37:5858-5872
McKenzie, Andrew T; Wang, Minghui; Hauberg, Mads E et al. (2018) Brain Cell Type Specific Gene Expression and Co-expression Network Architectures. Sci Rep 8:8868
Sekiya, Michiko; Wang, Minghui; Fujisaki, Naoki et al. (2018) Integrated biology approach reveals molecular and pathological interactions among Alzheimer's A?42, Tau, TREM2, and TYROBP in Drosophila models. Genome Med 10:26
Lin, Luan; Chen, Quan; Hirsch, Jeanne P et al. (2018) Temporal genetic association and temporal genetic causality methods for dissecting complex networks. Nat Commun 9:3980
Li, Zeran; Del-Aguila, Jorge L; Dube, Umber et al. (2018) Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure. Genome Med 10:43
Audrain, Mickael; Haure-Mirande, Jean-Vianney; Wang, Minghui et al. (2018) Integrative approach to sporadic Alzheimer's disease: deficiency of TYROBP in a tauopathy mouse model reduces C1q and normalizes clinical phenotype while increasing spread and state of phosphorylation of tau. Mol Psychiatry :
Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor et al. (2018) EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy. Bioinformatics 34:3151-3159
Peters, Lauren A; Perrigoue, Jacqueline; Mortha, Arthur et al. (2017) A functional genomics predictive network model identifies regulators of inflammatory bowel disease. Nat Genet 49:1437-1449

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