Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach

Kanchana Padmanabhan; Kelly Nudelman; Steve Harenberg; Gonzalo Bello; Dongwha Sohn; Katie Shpanskaya; Priyanka Tiwari Dikshit; Pallavi S. Yerramsetty; Rudolph E. Tanzi; Andrew J. Saykin; Jeffrey R. Petrella; P. Murali Doraiswamy; Nagiza F. Samatova; Alzheimer’s Disease Neuroimaging Initiative

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2018.0251

Published Article

LAPSE:2018.0251

Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach

Kanchana Padmanabhan, Kelly Nudelman, Steve Harenberg, Gonzalo Bello, Dongwha Sohn, Katie Shpanskaya, Priyanka Tiwari Dikshit, Pallavi S. Yerramsetty, Rudolph E. Tanzi, Andrew J. Saykin, Jeffrey R. Petrella, P. Murali Doraiswamy, Nagiza F. Samatova, Alzheimer’s Disease Neuroimaging Initiative

July 31, 2018

Alzheimer’s disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease mechanisms at a pathway level. However, to the best of our knowledge, no prior study has characterized the nature of pathway crosstalks in AD, or examined their utility as biomarkers for diagnosis or prognosis. In this paper, we build the first computational crosstalk model of AD incorporating genetics, antecedent knowledge, and biomarkers from a national study to create a generic pathway crosstalk reference map and to characterize the nature of genetic and protein pathway crosstalks in mild cognitive impairment (MCI) subjects. We perform initial studies of the utility of incorporating these crosstalks as biomarkers for assessing the risk of MCI progression to AD dementia. Our analysis identified Single Nucleotide Polymorphism-enriched pathways representing six of the seven Kyoto Encyclopedia of Genes and Genomes pathway categories. Integrating pathway crosstalks as a predictor improved the accuracy by 11.7% compared to standard clinical parameters and apolipoprotein E ε4 status alone. Our findings highlight the importance of moving beyond discrete biomarkers to studying interactions among complex biological pathways.

Record ID

LAPSE:2018.0251

Keywords

Alzheimer’s disease, biomarker, disease prediction, pathway crosstalk

Subject

Biosystems

Suggested Citation

Padmanabhan K, Nudelman K, Harenberg S, Bello G, Sohn D, Shpanskaya K, Tiwari Dikshit P, Yerramsetty PS, Tanzi RE, Saykin AJ, Petrella JR, Doraiswamy PM, Samatova NF, Alzheimer’s Disease Neuroimaging Initiative. Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach. (2018). LAPSE:2018.0251

Author Affiliations

Padmanabhan K: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA; Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Nudelman K: Indiana Alzheimer Disease Center and the Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Harenberg S: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
Bello G: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
Sohn D: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA; Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Shpanskaya K: Department of Radiology, Stanford University School of Medicine, Stanford, CA 94025, USA
Tiwari Dikshit P: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
Yerramsetty PS: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
Tanzi RE: Genetics and Aging Research Unit and Department of Neurology, Massachusetts General Hospital and Harvard Medical School Stanford University School of Medicine, Stanford, CA 02129, USA [ORCID]
Saykin AJ: Indiana Alzheimer Disease Center and the Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Petrella JR: Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
Doraiswamy PM: Neurocognitive Disorders Program, Department of Psychiatry and the Duke Institute for Brain Sciences, Duke University Health System, Durham, NC 27710, USA
Samatova NF: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA; Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Alzheimer’s Disease Neuroimaging Initiative:
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Journal Name

Processes

Volume

5

Issue

3

Article Number

E47

Year

2017

Publication Date

2017-08-17