# Integrative analysis of proteomics and transcriptomics to delineate vesicular transport related protein abnormalities related to Alzheimer's, Lewy body and mixed Pathologies

> **NIH NIH R21** · CLEVELAND CLINIC LERNER COM-CWRU · 2022 · $455,583

## Abstract

PROJECT SUMMARY/ABSTRACT
Dementia has a significant disease burden worldwide with around 50 million people having it. Alzheimer’s
disease (AD), the most common etiology of dementia is characterized by abnormal accumulation of amyloid
beta(Aβ) and tau proteins in the brain. After AD, dementia with Lewy bodies (DLB) is often noted among the top
two common forms of dementia and includes α-synuclein protein (α-syn) neuronal inclusions as a key pathology
marker. Even as AD and DLB are common, neuropathology studies have consistently noted that rather than a
single underlying etiology, there is a high frequency of patients where more than one pathology (mixed pathology)
contributes to the dementia syndrome. The pathophysiological impact of these pathologies when together, on
the initiation and progression of neurodegeneration and development of cognitive decline is yet to be
comprehensively understood.
The research at the foundation of this R21 is a clinical translational study that uses systems biology and
proteomics techniques to characterize the properties of differentially expressed genes and proteins between
patients with AD or DLB pathology alone and among those patients with mixed dementia pathology of AD and
DLB pathology. In this work we will clarify if there is a mechanistic reason some brains are more prone to develop
mixed pathology of ADP-LRP (Aβ-α-syn) over others that have predominantly Aβ or α-syn. Prior studies in AD
and DLB suggest dysregulation of endosomal-lysosomal pathways. As clinical AD dementia in these prior studies
often included mixed pathology, it has been challenging to disentangle the role for endosomal-lysosomal
dysregulation for each individual pathology. Our preliminary data leads us hypothesize that presence of Aβ
pathology in the brain along with co-existing cellular vesicular transport abnormalities makes it more likely for
the development of mixed pathologies including ADP-LRP. We will evaluate this hypothesis by proteomic and
transcriptomic evaluation of brain, CSF and plasma of three patient groups (AD, DLB and mixed AD-DLB) and
among age and sex matched normal controls. We will confirm and validate our data against data from other
large national data (ADNI, Accelerating Medicines Partnership-AD). We will develop a model paradigm to assess
the role for dysregulation of vesicular transport proteins in mixed pathologies at the genetic, transcriptional and
proteomic levels. If the hypothesis and models are validated, scientific insights from this research will help identify
the nature of synergistic relationship between these pathologies to develop better therapeutic targeting of mixed
pathology dementia. If the hypothesis is found to be not true, the findings from this study will still represent a
significant advance in our knowledge of variability in individual proteomic and transcriptomic signatures in the
face of neurodegenerative disease pathology. The results of this will be useful both clinically and in desig...

## Key facts

- **NIH application ID:** 10444050
- **Project number:** 1R21AG074287-01A1
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** Gurkan Bebek
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $455,583
- **Award type:** 1
- **Project period:** 2022-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10444050

## Citation

> US National Institutes of Health, RePORTER application 10444050, Integrative analysis of proteomics and transcriptomics to delineate vesicular transport related protein abnormalities related to Alzheimer's, Lewy body and mixed Pathologies (1R21AG074287-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10444050. Licensed CC0.

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