Integrated analysis of differential gene expression profiles in hippocampi to identify candidate genes involved in Alzheimer's disease

Hu,Wanhua; Lin,Xiaodong; Chen,Kelong

doi:10.3892/mmr.2015.4271

Integrated analysis of differential gene expression profiles in hippocampi to identify candidate genes involved in Alzheimer's disease

Authors:
- Wanhua Hu
- Xiaodong Lin
- Kelong Chen
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Affiliations: Department of Neurology, Wenzhou Hospital of Traditional Chinese Medicine, Wenzhou, Zhejiang 325000, P.R. China, Department of Traditional Chinese Internal Medicine, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang 325005, P.R. China
Published online on: August 28, 2015 https://doi.org/10.3892/mmr.2015.4271
Pages: 6679-6687
Copyright: © Hu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Alzheimer's disease (AD) is a complex neurodegenerative disorder with largely unknown genetic mechanisms. Identifying altered neuronal gene expression in AD may provide diagnostic or therapeutic targets for AD. The present study aimed to identify differentially expressed genes (DEGs) and their further association with other biological processes that regulate causative factors for AD. The present study performed an integrated analysis of publicly available gene expression omnibus datasets of AD hippocampi. Gene ontology (GO) enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein‑Protein interaction (PPI) network analysis were performed. The present study detected 295 DEGs (109 upregulated and 186 downregulated genes) in hippocampi between AD and control samples by integrating four datasets of gene expression profiles of hippocampi of patients with AD. Respiratory electron transport chain (GO: 0022904; P=1.64x10‑11) was the most significantly enriched GO term among biological processes, while for molecular functions, the most significantly enriched GO term was that of protein binding (GO: 0005515; P=3.03x10‑29), and for cellular components, the most significantly enriched GO term was that of the cytoplasm (GO: 0005737; P=8.67x10‑33). The most signiﬁcant pathway in the KEGG analysis was oxidative phosphorylation (P=1.61x10‑13). PPI network analysis showed that the significant hub proteins contained β-actin (degree, 268), hepatoma-derived growth factor (degree, 218) and WD repeat‑containing protein 82 (degree, 87). The integrated analysis performed in the present study serves as a basis for identifying novel drug targets to develop improved therapies and interventions for common and devastating neurological diseases such as AD.

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1	Hommet C, Mondon K, Constans T, Beaufils E, Desmidt T, Camus V and Cottier JP: Review of cerebral microangiopathy and Alzheimer's disease: Relation between white matter hyperintensities and microbleeds. Dement Geriatr Cogn Disord. 32:367–378. 2011. View Article : Google Scholar
2	Hyman BT, Van Hoesen GW, Damasio AR and Barnes CL: Alzheimer's disease: Cell-specific pathology isolates the hippocampal formation. Science. 225:1168–1170. 1984. View Article : Google Scholar : PubMed/NCBI
3	Kordower JH, Chu Y, Stebbins GT, DeKosky ST, Cochran EJ, Bennett D and Mufson EJ: Loss and atrophy of layer II entorhinal cortex neurons in elderly people with mild cognitive impairment. Ann Neurol. 49:202–213. 2001. View Article : Google Scholar : PubMed/NCBI
4	Scheff SW, Price DA, Schmitt FA, DeKosky ST and Mufson EJ: Synaptic alterations in CA1 in mild Alzheimer disease and mild cognitive impairment. Neurology. 68:1501–1508. 2007. View Article : Google Scholar : PubMed/NCBI
5	Kerchner GA, Hess CP, Hammond-Rosenbluth KE, Xu D, Rabinovici GD, Kelley DA, Vigneron DB, Nelson SJ and Miller BL: Hippocampal CA1 apical neuropil atrophy in mild Alzheimer disease visualized with 7-T MRI. Neurology. 75:1381–1387. 2010. View Article : Google Scholar : PubMed/NCBI
6	O'Brien RJ and Wong PC: Amyloid precursor protein processing and Alzheimer's disease. Annu Rev Neurosci. 34:185–204. 2011. View Article : Google Scholar : PubMed/NCBI
7	Noble W, Hanger DP, Miller CC and Lovestone S: The importance of tau phosphorylation for neurodegenerative diseases. Front Neurol. 4:832013. View Article : Google Scholar : PubMed/NCBI
8	Martin L, Latypova X, Wilson CM, Magnaudeix A, Perrin ML, Yardin C and Terro F: Tau protein kinases: Involvement in Alzheimer's disease. Ageing Res Rev. 12:289–309. 2013. View Article : Google Scholar
9	Liu CC, Kanekiyo T, Xu H and Bu G: Apolipoprotein E and Alzheimer disease: Risk, mechanisms and therapy. Nat Rev Neurol. 9:106–118. 2013. View Article : Google Scholar : PubMed/NCBI
10	Auld DS, Kornecook TJ, Bastianetto S and Quirion R: Alzheimer's disease and the basal forebrain cholinergic system: Relations to beta-amyloid peptides, cognition and treatment strategies. Prog Neurobiol. 68:209–245. 2002. View Article : Google Scholar : PubMed/NCBI
11	Beckmann L, Fischer C, Deck KG, Nolte IM, te Meerman G and Chang-Claude J: Exploring haplotype sharing methods in general and isolated populations to detect gene(s) of a complex genetic trait. Genet Epidemiol. 21(Suppl 1): S554–S559. 2001.
12	Blalock EM, Geddes JW, Chen KC, Porter NM, Markesbery WR and Landfield PW: Incipient Alzheimer's disease: Microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc Natl Acad Sci USA. 101:2173–2178. 2004. View Article : Google Scholar : PubMed/NCBI
13	Colangelo V, Schurr J, Ball MJ, Pelaez RP, Bazan NG and Lukiw WJ: Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: Transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling. J Neurosci Res. 70:462–473. 2002. View Article : Google Scholar : PubMed/NCBI
14	Mufson EJ, Counts SE and Ginsberg SD: Gene expression profiles of cholinergic nucleus basalis neurons in Alzheimer's disease. Neurochem Res. 27:1035–1048. 2002. View Article : Google Scholar : PubMed/NCBI
15	Siddiqui AS, Delaney AD, Schnerch A, Griffith OL, Jones SJ and Marra MA: Sequence biases in large scale gene expression profiling data. Nucleic Acids Res. 34:e832006. View Article : Google Scholar : PubMed/NCBI
16	Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41:D991–D995. 2013. View Article : Google Scholar
17	Altermann E and Klaenhammer TR: PathwayVoyager: Pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genomics. 6:602005. View Article : Google Scholar : PubMed/NCBI
18	Tabas-Madrid D, Nogales-Cadenas R and Pascual-Montano A: GeneCodis3: A non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res. 40:W478–W483. 2012. View Article : Google Scholar : PubMed/NCBI
19	Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E, et al: A protein interaction map of Drosophila melanogaster. Science. 302:1727–1736. 2003. View Article : Google Scholar : PubMed/NCBI
20	Schaefer MH, Lopes TJ, Mah N, Shoemaker JE, Matsuoka Y, Fontaine JF, Louis-Jeune C, Eisfeld AJ, Neumann G, Perez-Iratxeta C, et al: Adding protein context to the human protein-protein interaction network to reveal meaningful interactions. PLoS Comput Biol. 9:e10028602013. View Article : Google Scholar : PubMed/NCBI
21	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
22	Miller JA, Woltjer RL, Goodenbour JM, Horvath S and Geschwind DH: Genes and pathways underlying regional and cell type changes in Alzheimer's disease. Genome Med. 5:482013. View Article : Google Scholar : PubMed/NCBI
23	Hokama M, Oka S, Leon J, Ninomiya T, Honda H, Sasaki K, Iwaki T, Ohara T, Sasaki T, LaFerla FM, et al: Altered expression of diabetes-related genes in Alzheimer's disease brains: The Hisayama study. Cereb Cortex. 24:2476–2488. 2014. View Article : Google Scholar :
24	Liang WS, Dunckley T, Beach TG, Grover A, Mastroeni D, Walker DG, Caselli RJ, Kukull WA, McKeel D, Morris JC, et al: Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol Genomics. 28:311–322. 2007. View Article : Google Scholar
25	Meagher MJ, Schumacher JM, Lee K, Holdcraft RW, Edelhoff S, Disteche C and Braun RE: Identification of ZFR, an ancient and highly conserved murine chromosome-associated zinc finger protein. Gene. 228:197–211. 1999. View Article : Google Scholar : PubMed/NCBI
26	Kleines M, Gärtner A, Ritter K and Schaade L: Cloning and expression of the human single copy homologue of the mouse zinc finger protein zfr. Gene. 275:157–162. 2001. View Article : Google Scholar : PubMed/NCBI
27	Novarino G, Fenstermaker AG, Zaki MS, Hofree M, Silhavy JL, Heiberg AD, Abdellateef M, Rosti B, Scott E, Mansour L, et al: Exome sequencing links corticospinal motor neuron disease to common neurodegenerative disorders. Science. 343:506–511. 2014. View Article : Google Scholar : PubMed/NCBI
28	Zimmer DB, Chaplin J, Baldwin A and Rast M: S100-mediated signal transduction in the nervous system and neurological diseases. Cell Mol Biol (Noisy-le-grand). 51:201–214. 2005.
29	Boom A, Pochet R, Authelet M, Pradier L, Borghgraef P, Van Leuven F, Heizmann CW and Brion JP: Astrocytic calcium/zinc binding protein S100A6 over expression in Alzheimer's disease and in PS1/APP transgenic mice models. Biochim Biophys Acta. 1742:161–168. 2004. View Article : Google Scholar : PubMed/NCBI
30	Bellucci C, Lilli C, Baroni T, Parnetti L, Sorbi S, Emiliani C, Lumare E, Calabresi P, Balloni S and Bodo M: Differences in extracellular matrix production and basic fibroblast growth factor response in skin fibroblasts from sporadic and familial Alzheimer's disease. Mol Med. 13:542–550. 2007. View Article : Google Scholar : PubMed/NCBI
31	Leduc V, Legault V, Dea D and Poirier J: Normalization of gene expression using SYBR green qPCR: A case for paraoxonase 1 and 2 in Alzheimer's disease brains. J Neurosci Methods. 200:14–19. 2011. View Article : Google Scholar : PubMed/NCBI
32	Bamburg JR and Bloom GS: Cytoskeletal pathologies of Alzheimer disease. Cell Motil Cytoskeleton. 66:635–649. 2009. View Article : Google Scholar : PubMed/NCBI
33	Liang WS, Reiman EM, Valla J, Dunckley T, Beach TG, Grover A, Niedzielko TL, Schneider LE, Mastroeni D, Caselli R, et al: Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc Natl Acad Sci USA. 105:4441–4446. 2008. View Article : Google Scholar : PubMed/NCBI