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International Journal of Alzheimer’s Disease
Volume 2014 (2014), Article ID 721453, 18 pages
Research Article

Differential Network Analyses of Alzheimer’s Disease Identify Early Events in Alzheimer’s Disease Pathology

1Department of Mathematics, University of California, Davis, CA 95616, USA
2Division of Biostatistics, School of Medicine, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
3Department of Biology, University of Texas, San Antonio, TX 78249, USA
4Neurosciences Institute, University of Texas, San Antonio, TX 78249, USA

Received 25 January 2014; Revised 13 June 2014; Accepted 18 June 2014; Published 23 July 2014

Academic Editor: Suzanne M. de la Monte

Copyright © 2014 Jing Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In late-onset Alzheimer’s disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with low topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early events occurring in AD pathology. Cellular defense mechanisms try to intervene but fail, finally resulting in AD pathology as the disease progresses. Furthermore, disease annotation of the low TO genes in two independent protein interaction networks has resulted in association between cancer, diabetes, renal diseases, and cardiovascular diseases.