Alzheimer's disease (AD) pathology is characterized by the presence of phosphorylated tau in neurofibrillary tangles (NFTs), dystrophic neurites and abundant extracellular ?-amyloid in senile plaques. However, the etiology of AD remains elusive, partly due to the wide spectrum of clinical and neurobiological/neuropathological features in AD patients. Thus, heterogeneity in AD has complicated the task of discovering disease-modifying treatments and developing accurate in vivo indices for diagnosis and clinical prognosis. Different approaches have been proposed for AD subtyping, but they are generally neither suitable for high-dimensional data nor actionable due to the lack of mechanistic insights. Increased knowledge and understanding of different AD subtypes would shed light on recently failed clinical trials and provide for the potential to tailor treatments with specificity to more homogeneous subgroups of patients. By integrating genetic, molecular and neuroimaging data to more precisely define AD subtypes, we may be able to better discriminate between highly overlapping clinical phenotypes. Furthermore, the identification of such subtypes may potentially improve our understanding of its underlying pathomechanisms, prediction of its course, and the development of novel disease-modifying treatments. In this application, we propose to systematically identify and characterize molecular subtypes of AD by developing and employing cutting-edge network biology approaches to multiple existing large-scale genetic, gene expression, proteomic and functional MRI datasets. We will investigate the functional roles of key drivers underlying predicted AD subtypes as well as three candidate key drivers from our current AMP-AD consortia work in control and AD hiPSC-derived neural co-culture systems and then in complex organoids by screening the predicted transcriptional impact of top key drivers in single cell and cell-population-wide analyses. Functional assays in each cell type will be used to build evidence for relevance to AD-subtype phenotypes. Single cell RNA sequencing data will be generated to identify perturbation signatures in selected drivers that will then be mapped to subtype specific networks to build comprehensive signaling maps for each driver. The top three most promising drivers of AD subtypes and the three existing AMP-AD targets will be further validated using a) an independent postmortem cohort, and b) recombinant mice, including amyloidosis, tauopathy and new ?humanized? models.

Public Health Relevance

- This proposal aims to integrate large-scale molecular and imaging data to more precisely define and identify Alzheimer's disease (AD) subtypes in order to improve our understanding of AD pathomechanisms, the prediction of AD course, and the development of novel disease-modifying treatments. We will comprehensively characterize AD molecular subtypes and identify their key molecular network structures and key drivers for experimental validations using an independent human postmortem cohort, human iPSC derived brain cell cultures and AD transgenic mice.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZAG1)
Program Officer
Petanceska, Suzana
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Icahn School of Medicine at Mount Sinai
Schools of Medicine
New York
United States
Zip Code
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
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
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
Ortiz-Virumbrales, Maitane; Moreno, Cesar L; Kruglikov, Ilya et al. (2017) CRISPR/Cas9-Correctable mutation-related molecular and physiological phenotypes in iPSC-derived Alzheimer's PSEN2 N141I neurons. Acta Neuropathol Commun 5:77

Showing the most recent 10 out of 62 publications