# The effects of Alzheimer's disease risk genes on metabolism and signaling across cell types

> **NIH NIH RF1** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $3,944,334

## Abstract

Summary
Alzheimer's disease (AD) is pervasive and debilitating, with no truly effective treatments.
Genome wide association studies have found risk variants for sporadic, late-onset AD, but the
mechanisms driving this risk are still unknown. Two of the sAD variants with the highest
association with development of AD are in Apolipoprotein E (APOE) and ATP-binding cassette
transporter A7 (ABCA7), both of which are involved in lipid metabolism. Our prior work
demonstrates that the E4 allele of APOE (APOE4) has cell type specific effects, including
alterations in lipid metabolism, but important questions remain about the downstream pathways
affected by this allele. Critically, we do not know how APOE4-induced changes interact with
aging-related stress, leading to late-onset disease. Even less is known about how ABCA7
alleles lead to increased risk of AD. We propose to use a systems biology approach to discover
these AD-risk pathways, responding to NOT-AG-18-052 from the NIH, which designates
“systems biology of brain neural cells derived from human AD induced pluripotent stem cells” as
a high-priority research topic. Our approach uses multi-omic analysis of induced pluripotent
stem cell (iPSC) lines that are isogenic for two risk variants, APOE4 or ABCA7 premature
termination (PTC), which can then be differentiated into diverse cell types. Using an unbiased
approach, we will reveal how AD-risk alleles alter signaling, metabolism, and states of the cells,
how they affect individual cells as well as cell-cell interactions in complex cultures, and how they
alter cellular responses to acute stress. In Aim 1 we will deeply characterize the effects of
APOE4 and ABCA7 PTC in 2D culture models of neurons, astrocytes, microglia and pericytes,
differentiated from isogenic iPSC lines, examining changes in metabolism and post-translational
modifications (PTMs) of proteins. We will use advanced network optimization methods to
integrate the disparate data and to uncover molecular interaction networks that link together
changes observed in the individual omics. In Aim 2, we investigate the pathways altered by risk
alleles that influence cell-cell interactions in 3D culture models, using spatially-resolved PTM-
proteomics and metabolomics/lipidomics and causal computational models. In Aim 3, we will
examine the intersection of risk variant with environmental and cellular stressors in the 3D
culture models. Each aim includes rigorous testing of hypotheses in vitro and by examination of
postmortem samples.

## Key facts

- **NIH application ID:** 10524301
- **Project number:** 1RF1AG075901-01A1
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Ernest Fraenkel
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $3,944,334
- **Award type:** 1
- **Project period:** 2022-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10524301

## Citation

> US National Institutes of Health, RePORTER application 10524301, The effects of Alzheimer's disease risk genes on metabolism and signaling across cell types (1RF1AG075901-01A1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10524301. Licensed CC0.

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