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Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Research Article

Exploring Shared Pathogenesis of Alzheimer’s Disease and Type 2 Diabetes Mellitus via Co-expression Networks Analysis

Author(s): Yukun Zhu, Xuelu Ding, Zhaoyuan She, Xue Bai, Ziyang Nie, Feng Wang, Fei Wang and Xin Geng*

Volume 17, Issue 6, 2020

Page: [566 - 575] Pages: 10

DOI: 10.2174/1567205017666200810164932

Price: $65

Abstract

Background: Alzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (T2DM) have an increased incidence in modern society. Although increasing evidence has supported the close linkage between these two disorders, the inter-relational mechanisms remain to be fully elucidated.

Objective: The primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and T2DM.

Methods: We downloaded the microarray data of AD and T2DM from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene network modules related to AD and T2DM. Then, Gene Ontology (GO) and pathway enrichment analysis were performed on the common genes existing in the AD and T2DM related modules by clusterProfiler and DOSE package. Finally, we utilized the STRING database to construct the protein-protein interaction network and found out the hub genes in the network.

Results: Our findings indicated that seven and four modules were the most significant with AD and T2DM, respectively. Functional enrichment analysis showed that AD and T2DM common genes were mainly enriched in signaling pathways such as circadian entrainment, phagosome, glutathione metabolism and synaptic vesicle cycle. Protein-protein interaction network construction identified 10 hub genes (CALM1, LRRK2, RBX1, SLC6A1, TXN, SNRPF, GJA1, VWF, LPL, AGT) in AD and T2DM shared genes.

Conclusion: Our work identified common pathogenesis of AD and T2DM. These shared pathways might provide a novel idea for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosis and treatment of AD and T2DM.

Keywords: Alzheimer's disease, type 2 diabetes mellitus, WGCNA, functional enrichment analysis, protein-protein Interaction network, Hub gene.

[1]
Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 2010; 87(1): 4-14.
[http://dx.doi.org/10.1016/j.diabres.2009.10.007] [PMID: 19896746]
[2]
Xia X, Jiang Q, McDermott J, Han JJ. Aging and Alzheimer’s disease: Comparison and associations from molecular to system level. Aging Cell 2018; 17(5) e12802
[http://dx.doi.org/10.1111/acel.12802] [PMID: 29963744]
[3]
Reitz C, Brayne C, Mayeux R. Epidemiology of Alzheimer disease. Nat Rev Neurol 2011; 7(3): 137-52.
[http://dx.doi.org/10.1038/nrneurol.2011.2] [PMID: 21304480]
[4]
Baranello RJ, Bharani KL, Padmaraju V, et al. Amyloid-beta protein clearance and degradation (ABCD) pathways and their role in Alzheimer’s disease. Curr Alzheimer Res 2015; 12(1): 32-46.
[http://dx.doi.org/10.2174/1567205012666141218140953] [PMID: 25523424]
[5]
Nalivaeva NN, Belyaev ND, Kerridge C, Turner AJ. Amyloid-clearing proteins and their epigenetic regulation as a therapeutic target in Alzheimer’s disease. Front Aging Neurosci 2014; 6: 235.
[http://dx.doi.org/10.3389/fnagi.2014.00235] [PMID: 25278875]
[6]
Lee J, Kim DE, Griffin P, et al. Inhibition of REV-ERBs stimulates microglial amyloid-beta clearance and reduces amyloid plaque deposition in the 5XFAD mouse model of Alzheimer’s disease. Aging Cell 2020; 19(2) e13078
[PMID: 31800167]
[7]
Fernández-de Frutos M, Galán-Chilet I, Goedeke L, et al. MICRORNA 7 impairs insulin signaling and regulates Aβ levels through posttranscriptional regulation of the insulin receptor substrate 2, insulin receptor, insulin-degrading enzyme, and liver X receptor pathway. Mol Cell Biol 2019; 39(22): e00170-19.
[http://dx.doi.org/10.1128/MCB.00170-19] [PMID: 31501273]
[8]
Su M, Naderi K, Samson N, et al. Mechanisms Associated with type 2 diabetes as a risk factor for Alzheimer-related pathology. Mol Neurobiol 2019; 56(8): 5815-34.
[http://dx.doi.org/10.1007/s12035-019-1475-8] [PMID: 30684218]
[9]
Vagelatos NT, Eslick GD. Type 2 diabetes as a risk factor for Alzheimer’s disease: The confounders, interactions, and neuropathology associated with this relationship. Epidemiol Rev 2013; 35: 152-60.
[http://dx.doi.org/10.1093/epirev/mxs012] [PMID: 23314404]
[10]
Adeghate E, Donáth T, Adem A. Alzheimer disease and diabetes mellitus: Do they have anything in common? Curr Alzheimer Res 2013; 10(6): 609-17.
[http://dx.doi.org/10.2174/15672050113109990009] [PMID: 23627758]
[11]
Akter K, Lanza EA, Martin SA, Myronyuk N, Rua M, Raffa RB. Diabetes mellitus and Alzheimer’s disease: Shared pathology and treatment? Br J Clin Pharmacol 2011; 71(3): 365-76.
[http://dx.doi.org/10.1111/j.1365-2125.2010.03830.x] [PMID: 21284695]
[12]
Karki R, Kodamullil AT, Hofmann-Apitius M. Comorbidity analysis between Alzheimer’s disease and type 2 diabetes mellitus (T2DM) based on shared pathways and the role of T2DM drugs. J Alzheimers Dis 2017; 60(2): 721-31.
[http://dx.doi.org/10.3233/JAD-170440] [PMID: 28922161]
[13]
Lovestone S, Reynolds CH, Latimer D, et al. Alzheimer’s disease-like phosphorylation of the microtubule-associated protein tau by glycogen synthase kinase-3 in transfected mammalian cells. Curr Biol 1994; 4(12): 1077-86.
[http://dx.doi.org/10.1016/S0960-9822(00)00246-3] [PMID: 7704571]
[14]
Ge X, Yang Y, Sun Y, Cao W, Ding F. Islet amyloid polypeptide promotes amyloid-beta aggregation by binding-induced helix-unfolding of the amyloidogenic core. ACS Chem Neurosci 2018; 9(5): 967-75.
[http://dx.doi.org/10.1021/acschemneuro.7b00396] [PMID: 29378116]
[15]
Hirose H, Takayama M, Iwao Y, Kawabe H. Effects of aging on visceral and subcutaneous fat areas and on homeostasis model assessment of insulin resistance and insulin secretion capacity in a comprehensive health checkup. J Atheroscler Thromb 2016; 23(2): 207-15.
[http://dx.doi.org/10.5551/jat.30700] [PMID: 26412583]
[16]
Zhang J, Liu F. Tissue-specific insulin signaling in the regulation of metabolism and aging. IUBMB Life 2014; 66(7): 485-95.
[http://dx.doi.org/10.1002/iub.1293] [PMID: 25087968]
[17]
Bassil F, Fernagut PO, Bezard E, Meissner WG. Insulin, IGF-1 and GLP-1 signaling in neurodegenerative disorders: Targets for disease modification? Prog Neurobiol 2014; 118: 1-18.
[http://dx.doi.org/10.1016/j.pneurobio.2014.02.005] [PMID: 24582776]
[18]
Kurochkin IV, Guarnera E, Berezovsky IN. Insulin-degrading enzyme in the fight against Alzheimer’s disease. Trends Pharmacol Sci 2018; 39(1): 49-58.
[http://dx.doi.org/10.1016/j.tips.2017.10.008] [PMID: 29132916]
[19]
Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9: 559.
[http://dx.doi.org/10.1186/1471-2105-9-559] [PMID: 19114008]
[20]
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005; 4: Article 7.
[http://dx.doi.org/10.2202/1544-6115.1128]
[21]
Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: The Dynamic Tree Cut package for R. Bioinformatics 2008; 24(5): 719-20.
[http://dx.doi.org/10.1093/bioinformatics/btm563] [PMID: 18024473]
[22]
Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol 2007; 1: 54.
[http://dx.doi.org/10.1186/1752-0509-1-54] [PMID: 18031580]
[23]
Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module preserved and reproducible? PLOS Comput Biol 2011; 7(1) e1001057
[http://dx.doi.org/10.1371/journal.pcbi.1001057] [PMID: 21283776]
[24]
Oldham MC, Horvath S, Geschwind DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci USA 2006; 103(47): 17973-8.
[http://dx.doi.org/10.1073/pnas.0605938103] [PMID: 17101986]
[25]
Fuller TF, Ghazalpour A, Aten JE, Drake TA, Lusis AJ, Horvath S. Weighted gene coexpression network analysis strategies applied to mouse weight. Mamm Genome 2007; 18(6-7): 463-72.
[http://dx.doi.org/10.1007/s00335-007-9043-3] [PMID: 17668265]
[26]
Ghazalpour A, Doss S, Zhang B, et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet 2006; 2(8) e130
[http://dx.doi.org/10.1371/journal.pgen.0020130] [PMID: 16934000]
[27]
Horvath S, Zhang B, Carlson M, et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA 2006; 103(46): 17402-7.
[http://dx.doi.org/10.1073/pnas.0608396103] [PMID: 17090670]
[28]
Yu G, Wang LG, Han Y, He QY. Cluster profiler: An R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[29]
Yu G, Wang LG, Yan GR, He QY. DOSE: An R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 2015; 31(4): 608-9.
[http://dx.doi.org/10.1093/bioinformatics/btu684] [PMID: 25677125]
[30]
Szklarczyk D, Morris JH, Cook H, et al. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2017; 45(D1): D362-8.
[http://dx.doi.org/10.1093/nar/gkw937] [PMID: 27924014]
[31]
Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498-504.
[http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]
[32]
Copple IM, den Hollander W, Callegaro G, et al. Characterisation of the NRF2 transcriptional network and its response to chemical insult in primary human hepatocytes: Implications for prediction of drug-induced liver injury. Arch Toxicol 2019; 93(2): 385-99.
[http://dx.doi.org/10.1007/s00204-018-2354-1] [PMID: 30426165]
[33]
Zhai X, Xue Q, Liu Q, Guo Y, Chen Z. Colon cancer recurrence-associated genes revealed by WGCNA co-expression network analysis. Mol Med Rep 2017; 16(5): 6499-505.
[http://dx.doi.org/10.3892/mmr.2017.7412] [PMID: 28901407]
[34]
Pei G, Chen L, Zhang W. WGCNA Application to proteomic and metabolomic data analysis. Methods Enzymol 2017; 585: 135-58.
[http://dx.doi.org/10.1016/bs.mie.2016.09.016] [PMID: 28109426]
[35]
Son SM, Song H, Byun J, et al. Accumulation of autophagosomes contributes to enhanced amyloidogenic APP processing under insulin-resistant conditions. Autophagy 2012; 8(12): 1842-4.
[http://dx.doi.org/10.4161/auto.21861] [PMID: 22931791]
[36]
Zhao N, Liu CC, Van Ingelgom AJ, et al. Apolipoprotein E4 impairs neuronal insulin signaling by trapping insulin receptor in the endosomes. Neuron 2017; 96(1): 115.
[http://dx.doi.org/10.1016/j.neuron.2017.09.003]
[37]
Sposato V, Canu N, Fico E, et al. The medial septum is insulin resistant in the ad presymptomatic phase: Rescue by nerve growth factor-driven IRS1 activation. Mol Neurobiol 2019; 56(4): 3068.
[38]
Gubin DG, Nelaeva AA, Uzhakova AE, Hasanova YV, Cornelissen G, Weinert D. Disrupted circadian rhythms of body temperature, heart rate and fasting blood glucose in prediabetes and type 2 diabetes mellitus. Chronobiol Int 2017; 34(8): 1136-48.
[http://dx.doi.org/10.1080/07420528.2017.1347670] [PMID: 28759269]
[39]
Blume C, Lechinger J, Santhi N, et al. Significance of circadian rhythms in severely brain-injured patients: A clue to consciousness? Neurology 2017; 88(20): 1933-41.
[http://dx.doi.org/10.1212/WNL.0000000000003942] [PMID: 28424270]
[40]
Esteras N, Muñoz Ú, Alquézar C, Bartolomé F, Bermejo-Pareja F, Martín-Requero Á. Altered calmodulin degradation and signaling in non-neuronal cells from Alzheimer’s disease patients. Curr Alzheimer Res 2012; 9(3): 267-77.
[http://dx.doi.org/10.2174/156720512800107564] [PMID: 22044025]
[41]
Rieker C, Migliavacca E, Vaucher A, et al. Apolipoprotein E4 expression causes gain of toxic function in isogenic human induced pluripotent stem cell-derived endothelial cells. Arterioscler Thromb Vasc Biol 2019; 39(9): e195-207.
[http://dx.doi.org/10.1161/ATVBAHA.118.312261] [PMID: 31315437]

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