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Aggregate Interactome Based on Protein-Crosslinking Interfaces Predicts Drug Targets to Limit Aggregation in Neurodegenerative Diseases

41 Pages Posted: 1 Apr 2019 Sneak Peek Status: Review Complete

See all articles by Meenakshisundaram Balasubramaniam

Meenakshisundaram Balasubramaniam

Central Arkansas Veterans Healthcare Service - McClellan Veterans Medical Center; University of Arkansas for Medical Sciences, Reynolds Institute on Aging, Department of Geriatrics

Srinivas Ayyadevara

Central Arkansas Veterans Healthcare Service - McClellan Veterans Medical Center; University of Arkansas for Medical Sciences, Reynolds Institute on Aging, Department of Geriatrics

Akshatha Ganne

University of Arkansas for Medical Sciences - Bioinformatics Program; University of Arkansas at Little Rock

Samuel Kakraba

University of Arkansas for Medical Sciences - Bioinformatics Program; University of Arkansas at Little Rock

Narsimha Reddy Penthala

University of Arkansas for Medical Sciences, College of Pharmacy, Department of Pharmaceutical Sciences

Xiuxia Du

University of North Carolina (UNC) at Charlotte - Department of Bioinformatics & Genomics

Peter A. Crooks

University of Arkansas for Medical Sciences, College of Pharmacy, Department of Pharmaceutical Sciences

Sue T. Griffin

Central Arkansas Veterans Healthcare Service - McClellan Veterans Medical Center; University of Arkansas for Medical Sciences, Reynolds Institute on Aging, Department of Geriatrics

Robert J. Shmookler Reis

Central Arkansas Veterans Healthcare Service - McClellan Veterans Medical Center; University of Arkansas for Medical Sciences, Reynolds Institute on Aging, Department of Geriatrics

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Abstract

Diagnosis of neurodegenerative diseases hinges on detection of “seed” proteins in disease-specific aggregates. These inclusions contain diverse constituents, adhering through aberrant interactions that our prior data indicate are nonrandom. To define preferential protein-protein contacts mediating aggregate coalescence, we created click-chemistry reagents that crosslink neighboring proteins within human, APPSw-driven, neuroblastoma-cell aggregates. These reagents incorporate a biotinyl group to efficiently recover linked tryptic-peptide pairs. Mass-spectroscopy outputs were screened for all possible peptide pairs in the aggregate proteome. These empirical linkages, ranked by abundance, implicate a protein-adherence network termed the “aggregate-contactome.” Critical hubs and hub-hub interactions were assessed by RNAi-mediated rescue of chemotaxis in aging nematodes, and aggregation-driving properties were inferred by multivariate-regression and neural-network approaches. Aspirin, while disrupting aggregation, greatly simplified the aggregate contactome. This approach, and the dynamic model of aggregate accrual it implies, reveal the architecture of insoluble-aggregate networks and highlight influential proteins asnovel targets to ameliorate protein-aggregation diseases.

Keywords: Alzheimer’s Disease, Neurodegeneration, Protein Aggregation, Protein Interaction, NSAID, Nonsteroidal Anti-Inflammatory Drug, Disordered Proteins, Neural Network

Suggested Citation

Balasubramaniam, Meenakshisundaram and Ayyadevara, Srinivas and Ganne, Akshatha and Kakraba, Samuel and Penthala, Narsimha Reddy and Du, Xiuxia and Crooks, Peter A. and Griffin, Sue T. and Reis, Robert J. Shmookler, Aggregate Interactome Based on Protein-Crosslinking Interfaces Predicts Drug Targets to Limit Aggregation in Neurodegenerative Diseases (March 29, 2019). Available at SSRN: https://ssrn.com/abstract=3362341 or http://dx.doi.org/10.2139/ssrn.3362341
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Meenakshisundaram Balasubramaniam

Central Arkansas Veterans Healthcare Service - McClellan Veterans Medical Center

Little Rock, AR
United States

University of Arkansas for Medical Sciences, Reynolds Institute on Aging, Department of Geriatrics

Little Rock, AR
United States

Srinivas Ayyadevara

Central Arkansas Veterans Healthcare Service - McClellan Veterans Medical Center ( email )

Little Rock, AR
United States

University of Arkansas for Medical Sciences, Reynolds Institute on Aging, Department of Geriatrics ( email )

Little Rock, AR
United States

Akshatha Ganne

University of Arkansas for Medical Sciences - Bioinformatics Program

Little Rock, AR
United States

University of Arkansas at Little Rock

Little Rock, AR 72201
United States

Samuel Kakraba

University of Arkansas for Medical Sciences - Bioinformatics Program

Little Rock, AR
United States

University of Arkansas at Little Rock

Little Rock, AR 72201
United States

Narsimha Reddy Penthala

University of Arkansas for Medical Sciences, College of Pharmacy, Department of Pharmaceutical Sciences

Little Rock, AR
United States

Xiuxia Du

University of North Carolina (UNC) at Charlotte - Department of Bioinformatics & Genomics

Little Rock, AR
United States

Peter A. Crooks

University of Arkansas for Medical Sciences, College of Pharmacy, Department of Pharmaceutical Sciences

Little Rock, AR
United States

Sue T. Griffin

Central Arkansas Veterans Healthcare Service - McClellan Veterans Medical Center

Little Rock, AR
United States

University of Arkansas for Medical Sciences, Reynolds Institute on Aging, Department of Geriatrics

Little Rock, AR
United States

Robert J. Shmookler Reis (Contact Author)

Central Arkansas Veterans Healthcare Service - McClellan Veterans Medical Center ( email )

Little Rock, AR
United States

University of Arkansas for Medical Sciences, Reynolds Institute on Aging, Department of Geriatrics ( email )

Little Rock, AR
United States

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