# Integrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's Disease

> **NIH NIH U01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $164,076

## Abstract

Project Summary
Alzheimer's disease (AD) pathology is characterized by the presence of phosphorylated tau in
neurofibrillary tangles (NFTs), dystrophic neurites and abundant extracellular β-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.

## Key facts

- **NIH application ID:** 10126510
- **Project number:** 3U01AG046170-07S2
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** MICHELLE E EHRLICH
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $164,076
- **Award type:** 3
- **Project period:** 2014-05-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10126510

## Citation

> US National Institutes of Health, RePORTER application 10126510, Integrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's Disease (3U01AG046170-07S2). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10126510. Licensed CC0.

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