Alzheimer's disease (AD) is a debilitating neurodegenerative disorder. Pathologically, AD is characterized by amyloid plaques and neurofibrillary tangles. Clinically, AD patients present with progressive memory decline followed by deterioration of other cognitive domains and activities of daily living. Advanced age is the greatest risk factor. No effective method is available for preventing and/or treating this devastating disease. However, certain individuals of the elder population (? 85 years) remain cognitively intact, including some with substantial plaques and neurofibrillary tangle burdens, the two pathological hallmarks for fully symptomatic AD. The mechanisms of cognitive resilience and protection against AD in these elderly persons remain elusive. This proposal brings together scientists and postmortem human brain tissue samples from two major AD-research centers (the Icahn School of Medicine at Mount Sinai and the Rush University Medical Center) and aims to systematically identify and validate genetic variants, genes, proteins, and molecular networks underlying cognitive resilience to AD risk and proposes to build a comprehensive unbiased signaling pathway map underlying cognitive resilience to AD. Towards this end, we will develop an AD resilient cohort comprised of genetic, transcriptomic and proteomic data in the prefrontal cortex from a large number of brains in four categories: 1) very old (age of death (AoD) ? 85) AD-resilient, 2) young (AoD < 85) healthy, 3) very old (AoD ?85) AD and 4) young (AoD < 85) AD. We will perform systems genetics and integrative network biology analyses on the large-scale high-dimensional molecular profiling data to identify genetic variants, genes, proteins, and molecular networks underlying cognitive resilience to AD risk. We will systematically validate key drivers of the molecular networks underlying the cognitive resilience to AD using two diverse (C. elegans and mouse) model systems. We will validate the structures of AD-resilient molecular networks for building a data- driven, comprehensive signaling pathway map underlying cognitive resilience to AD risk. In particular, we will test the hypotheses that enhanced mitochondrial function and immune competence as well as their underlying molecular networks confer cognitive resilience. Our study will not only present a global landscape of the interplays among genetic variants, mRNAs and proteins responsible for cognitive resilience to AD but also pinpoint critical network structures and key drivers that can potentially lead to development of novel prevention strategies in combating AD.
This proposal aims to systematically develop and validate molecular network models underlying cognitive resilience to AD risk. We will generate and analyze an AD-resilient dataset comprised of genetic, transcriptomic and proteomic data from a large number of very old and AD-resilient human brains and build network models of cognitive resilience to AD that will be extensively validated in C. elegans and mouse models of AD.
|Wang, Minghui; Beckmann, Noam D; Roussos, Panos et al. (2018) The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease. Sci Data 5:180185|
|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|
|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|
|Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor et al. (2018) EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy. Bioinformatics 34:3151-3159|
|McKenzie, Andrew T; Moyon, Sarah; Wang, Minghui et al. (2017) Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease. Mol Neurodegener 12:82|