# Protein Stability Profiling for the Characterization of Alzheimer's Disease

> **NIH NIH R21** · DUKE UNIVERSITY · 2022 · $423,898

## Abstract

ABSTRACT
 A comprehensive understanding of the complex biological processes associated with
Alzheimer's Disease (AD) in mammalian systems requires their molecular characterization at
the systems level. Existing methods for such systems level analyses have typically involved the
measurement of gene and protein expression levels. Unfortunately, while the application of such
gene and protein expression level analyses to the characterization of AD has identified a few
biomarkers of the disease, the practical utility of these markers has been limited, especially for
the development of drug therapies. Thus, there is a need for additional research tools to better
understand AD pathogenesis and uncover more useful AD biomarkers. Proposed here is an
effort to investigate the use of large-scale protein folding stability measurements at the systems
level to characterize AD. In contrast to gene and protein expression level analyses, the protein
folding stability analyses proposed here are expected to report more directly on biologically
significant phenomena generally postulated to be responsible for AD pathogenesis such as the
mutation, modification, and misfolding of proteins.
 The proposed work will investigate the use of three mass spectrometry-based methods
for making protein folding stability measurements on the proteomic (including the Stability of
Proteins from Rates of Oxidation (SPROX) technique, the Thermal Proteome Profiling (TPP)
technique, and the Limited Proteolysis technique (LiP) to identify proteins with AD-related
changes in their folding stability using a transgenic mouse model of the disease (5XFAD). The
specific aims of this work are: (1) to generate protein folding stability profiles on mouse brain
proteins derived from the hippocampus region of 5XFAD and control B6SJLF1/J mice aged 2
and 8 months using 10 mice in each cohort; (2) to identify “hit” proteins with differential folding
stabilities in the age-matched 5XFAD and control B6SJLF1/J mice; and (3) to characterize the
“hit” proteins identified in (2) using bioinformatics tools and biochemical assays to better
understand their AD-related functions and disysfunctions.

## Key facts

- **NIH application ID:** 10524546
- **Project number:** 1R21AG074317-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Michael C Fitzgerald
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $423,898
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10524546

## Citation

> US National Institutes of Health, RePORTER application 10524546, Protein Stability Profiling for the Characterization of Alzheimer's Disease (1R21AG074317-01A1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10524546. Licensed CC0.

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