Open Access

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
  • View Affiliations

  • 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.

Introduction

Alzheimer's disease (AD) is one of the most common and complex neurodegenerative disorders and is characterized by a progressive decline of memory and cognition (1). The disease is defined by specific neuropathological changes of neurofibrillary tangles (NFT) and amyloid plaques that accumulate in vulnerable brain regions (2,3). Neurodegeneration in the development of AD varies substantially across cell types and regions. Of note, it has been demonstrated that hippocampal CA1 pyramidal neurons are particularly vulnerable to neurodegeneration and bear NFTs during the early stages of AD (4,5); however, the underlying mechanisms of their degeneration have remained elusive.

AD is thought to be caused by the dysregulation of a large number of genes and the consequent alteration of their complex interactions, which finally contributes to the broad spectrum of disease phenotypes (69). Microarray technology, which provides researchers with a tool to assess the expression levels of thousands of genes simultaneously, offers the possibility of gaining insight into gene networks disturbed in intricate human disease such as AD, and to obtain possible molecular clues regarding the underlying mechanisms of the pathophysiology of AD. Previous studies have used this technique to more comprehensively enhance the knowledge of the cellular and molecular changes underlying AD (1014). Although these studies have yielded significant novel insights, inconsistencies are present across these studies due to limitations based on small sample sizes and various results obtained by different groups with different laboratory protocols, microarray platforms and microarray data interpretations (15). In view of this, the present study integrated hippocampus gene expression datasets from multiple AD microarray studies to overcome these limitations of individual studies, resolve inconsistencies and provide significant novel insight into the complex biological processes involved in AD.

Materials and methods

Identification of eligible gene expression profiles of hippocampi of patients with AD

Hippocampal gene expression profiling studies in patients with AD were identified by searching the Gene Expression Omnibus database (GEO; http://www.ncbi.nlm.nih.gov/geo) (16). The following key words and their combinations were used: 'Alzheimer's disease', 'hippocampus', 'gene expression' and 'microarray.' Only experimental studies that had performed hippocampal gene expression profiling in patients with AD as well as normal control (NC) subjects were used. Non-human studies, review articles and integrated analyses of expression profiles were excluded.

Data preprocessing

Normalization is crucial for comparing different microarray datasets. The heterogeneity caused by different microarray platforms, gene nomenclature and clinical samples may make it difficult to compare the expression data directly. However, inappropriate normalization may contribute to the skewing of results and reduce their statistical significance. Consequently, a global normalization approach to minimize any inconsistencies should be included. For this propose, MATLAB Bioinformatics Toolbox was used in the present study to pre-process the raw microarray data of each study by Quantile normalization and log2 transformation to obtain intensity values.

Statistical analysis

MATLAB software, version 2013a (MathWorks, Natick, MA) was used to identify the differently expressed probe sets in the hippocampal tissues of patients with AD compared to those of NC subjects. A gene-specific t-test was performed, followed by calculation of the P-value and the effect size of the individual microarray study. Fisher's combined probability method was used to combine P-values from multiple studies, and the random effects model was used to combine effect sizes from multiple studies. Genes with an effect size >0.8 and a P-value <0.01 were selected as the significantly differentially expressed genes (DEGs).

Functional annotation of DEGs

To gain insight into the biological functions of DEGs, gene ontology (GO) classification was performed. GO provides a common descriptive framework as well as functional annotation and classification for analyzing the gene expression datasets. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) pathway enrichment analysis was performed to map the potential pathways of the DEGs. The KEGG pathway database is a recognized and comprehensive database, which includes an extensive variety of biochemical pathways (17). The online-based software GENECODIS, version 3 was utilized in the present analysis (18).

Protein-protein interactions (PPIs) network construction

PPI analysis allows for the assessment of protein functions at the molecular level, which are divided into the categories of cellular growth, development, metabolism, differentiation and apoptosis (19). The detection of key protein-interacting ions in the PPI networks of AD is important for the interpretation of cellular regulatory mechanisms in the development of the disease (20). The present study adopted the Search Tool for the Retrieval of Interacting Genes/Proteins (http://www.string-db.org/), a database of known and predicted protein interactions, to construct the PPI network and then visualized the distribution characteristics of the top 10 up- and downregulated DEGs in the network with Cytoscape software, version 3.2.0 (21).

Results

Identification of DEGs in hippocampi of patients with AD

The present study collected a total of four datasets of gene expression profiles in hippocampi of patients with AD according to the inclusion criteria; in total, data on the gene expression in 73 samples from patients with AD and 61 samples from control subjects were analyzed. The studies containing the individual hippocampal expression profiles in patients with AD are listed in Table I (12,2224). A total of 11,494 genes from four expression profiling studies were assessed. For the purpose of global normalization, the raw microarray data were pre-processed by Quantile normalization and log2 transformation to obtain intensity values for each probe, which were used in the gene expression profiling. Subsequently, MATLAB software was utilized to identify DEGs in hippocampi between patients with AD and control subjects. Finally, a total of 295 DEGs were regarded as significantly differentially expressed between samples of patients with AD and NC subjects (109 upregulated and 186 downregulated genes) when the threshold was set as P<0.01 and effect size >0.8. A list of the top 10 most significantly up- or downregulated genes is presented in Table II. The pattern of expressional changes of the top 50 most significantly DEGs is displayed in a heat map in Fig. 1.

Table I

Characteristics of the individual studies.

Table I

Characteristics of the individual studies.

GEO IDPlatformSamples (n) (cases:controls)CountryYearAuthor
GSE29378GPL6947 Illumina HumanHT-12
V3.0 expression beadchip
31:32USA2013Miller JA (22)
GSE36980GPL6244 [HuGene-1_0-st]
Affymetrix Human Gene 1.0 ST Array
7:10Japan2013Hokama M (23)
GSE5281GPL570 [HG-U133_Plus_2] Affymetrix
Human Genome U133 Plus 2.0 Array
13:10USA2007Liang WS (24)
GSE1297GPL96 [HG-U133A] Affymetrix
Human Genome U133A Array
22:9USA2004Blalock EM (12)

[i] GEO, Gene Expression Omnibus.

Table II

Top 10 most significantly up- or down-regulated differentially expressed genes.

Table II

Top 10 most significantly up- or down-regulated differentially expressed genes.

Gene IDGene symbolOfficial full nameP-valueEffect size
Upregulated genes
 51663ZFRZinc finger RNA binding protein 1.12×10−60.94853
 2669GEMGTP binding protein overexpressed in skeletal muscle 1.21×10−61.1503
 6277S100A6S100 calcium binding protein A6 2.74×10−61.0918
 80335WDR82WD repeat domain 82 3.05×10−60.80325
 5209PFKFB3 6-Phosphofructo-2-kinase/fructose-2, 6-Biphosphatase 3 3.24×10−61.1908
 3895KTN1Kinectin 1 (kinesin receptor) 3.36×10−61.1643
 3068HDGFHepatoma-derived growth factor (high-mobility group protein 1-like) 4.57×10−61.1580
 2077ERFEts2 repressor factor 5.16×10−61.0677
 7049TGFBR3Transforming growth factor, beta receptor III 5.34×10−61.1818
 5042PABPC3Poly(A) binding protein, cytoplasmic 3 7.09×10−61.0283
Downregulated genes
 22820COPG1Coatomer protein complex, subunit gamma 8.69×10−81.3017
 58189WFDC1WAP four-disulfide core domain 1 4.19×10−71.3844
 10093ARPC4Tubulin tyrosine ligase-like family, member 3; actin related protein 2/3 complex, subunit 4, 20kDa 7.43×10−71.1458
 60ACTBActin, beta 1.08×10−61.1657
 9158FIBPfibroblast growth factor (acidic) intracellular binding protein 1.15×10−61.1794
 56993TOMM22Translocase of outer mitochondrial membrane 22 homolog (yeast) 2.35×10−61.0773
 2537IFI6Interferon, alpha-inducible protein 6 2.57×10−61.1754
 9556C14orf2Chromosome 14 open reading frame 2 2.72×10−61.1734
 55837EAPPE2F-associated phosphoprotein 3.11×10−61.0270
 5889RAD51CRAD51 homolog C (S. cervisiae) 5.16×10−61.0461

The upregulated gene with the lowest P-value was ZFR, which is mainly expressed in neural tissue, but also weakly expressed in other tissue types (25,26), suggesting a neuronal function. A recent study identified ZFR as a putative genes associated with hereditary spastic paraplegias by using whole-exome sequencing (27). The downregulated gene with the lowest P-value was COPG1, whose function has yet to be determined.

Functional annotation

To investigate the biological roles of the DEGs in the hippocampi of patients with AD, the present study performed a categorized GO enrichment analysis. GO provides a common descriptive framework and functional annotation of the gene datasets. GO categories are separated into three groups: Biological processes, cellular components and molecular function. The present study examined GO categories separately using the web-based software GENECODIS. The results showed that genes associated with the respiratory electron transport chain (GO: 0022904; P=1.64×10−11) and gluconeogenesis (GO: 0006094; P=2.84×10−5) were significantly enriched among biological processes, while for molecular functions, protein binding (GO:0005515; P=3.03×10−29) and nucleotide binding (GO: 0000166; P=5.41×10−14) were significantly enriched, and with regard to cellular components, genes associated with the cytoplasm (GO:0005737; P=8.67×10−33) and mitochondrion (GO: 0005739; P=1.00×10−23) were significantly enriched (Table III, Fig. 2A).

Table III

GO terms of differentially expressed genes (top 15).

Table III

GO terms of differentially expressed genes (top 15).

GO IDGO termNo. of genesFDR
Biological processes
 GO:0022904Respiratory electron transport chain14 1.64×10−11
 GO:0006094 Gluconeogenesis7 2.84×10−5
 GO:0006006Glucose metabolic process9 4.94×10−5
 GO:0006200ATP catabolic process8 1.15×10−4
 GO:0010388Cullin deneddylation4 1.18×10−4
 GO:0006915Apoptotic process19 1.35×10−4
 GO:0006096Glycolysis6 2.91×10−4
 GO:0007264Small GTPase mediated signal transduction13 3.44×10−4
 GO:0042776Mitochondrial ATP synthesis coupled proton transport4 3.61×10−4
 GO:0015992Proton transport6 3.84×10−4
 GO:0007165Signal transduction26 7.19×10−4
 GO:0006120Mitochondrial electron transport, NADH to ubiquinone5 1.01×10−3
 GO:0006810Transport17 1.26×10−3
 GO:0048146Positive regulation of fibroblast proliferation5 1.30×10−3
 GO:0016071mRNA metabolic process10 1.40×10−3
Molecular function
 GO:0005515Protein binding117 3.03×10−29
 GO:0000166Nucleotide binding59 5.41×10−14
 GO:0005524ATP binding37 5.48×10−7
 GO:0005525GTP binding16 1.32×10−5
 GO:0046961Proton-transporting ATPase activity, rotational mechanism5 4.49×10−5
 GO:0003924GTPase activity12 4.55×10−5
 GO:0015631Tubulin binding5 8.56×10−5
 GO:0046933Hydrogen ion transporting ATP synthase activity, rotational mechanism4 3.08×10−4
 GO:0005509Calcium ion binding18 5.87×10−4
 GO:0008137NADH dehydrogenase (ubiquinone) activity5 6.24×10−4
 GO:0003713Transcription coactivator activity10 6.81×10−4
 GO:0022857Transmembrane transporter activity5 8.83×10−4
 GO:0005516Calmodulin binding8 1.17×10−3
 GO:0047485Protein N-terminus binding6 1.81×10−3
 GO:0003878ATP citrate synthase activity2 1.87×10−3
Cellular components
 GO:0005737Cytoplasm133 8.67×10−23
 GO:0005739Mitochondrion60 1.00×10−23
 GO:0005829Cytosol62 6.03×10−16
 GO:0005743Mitochondrial inner membrane25 1.38×10−15
 GO:0005634Nucleus101 4.71×10−14
 GO:0005856Cytoskeleton26 7.29×10−7
 GO:0005625Soluble fraction17 1.64×10−6
 GO:0016020Membrane66 3.04×10−6
 GO:0005886Plasma membrane60 3.94×10−6
 GO:0005759Mitochondrial matrix12 4.18×10−6
 GO:0005654Nucleoplasm25 4.28×10−6
 GO:0045121Membrane raft9 3.28×10−5
 GO:0005730Nucleolus31 4.98×10−5
 GO:0005753Mitochondrial proton-transporting ATP synthase complex4 1.58×10−4
 GO:0030054Cell junction15 2.50×10−4

[i] FDR, false discovery rate; GO, gene ontology.

The present study subsequently performed a KEGG pathway enrichment analysis in order to further evaluate the biological roles of the DEGs. A hypergeometric test with P<0.05 was used as the criterion for pathway detection. According to the KEGG analysis, oxidative phosphorylation was the most significant pathway (P=1.61×10−13). Furthermore, pathways involved in Parkinson's disease (P=3.77×10−10) and Huntington's disease (P=2.26×10−8) were also highly enriched (Table IV, Fig. 2B).

Table IV

KEGG pathways of differentially expressed genes (top 15).

Table IV

KEGG pathways of differentially expressed genes (top 15).

KEGG IDKEGG termNo. of genesFDRGenes
hsa00190Oxidative phosphorylation17 1.61×10−13COX4I1, NDUFAB1, UQCRC1, NDUFV2, COX6A1, COX6B1, ATP5L, ATP5J2, ATP6AP1, ATP6V1E1, NDUFA4, ATP5C1, NDUFA9, ATP6V1B2, NDUFS3, UQCRH, ATP5B
hsa05012Parkinson's disease14 3.77×10−10COX4I1, NDUFAB1, VDAC2, UQCRC1, NDUFV2, COX6A1, COX6B1, NDUFA4, ATP5C1, NDUFA9, UCHL1, NDUFS3, UQCRH, ATP5B
hsa05016Huntington's disease14 2.26×10−8COX4I1, NDUFAB1, VDAC2, UQCRC1, NDUFV2, COX6A1, COX6B1, EP300, NDUFA4, ATP5C1, NDUFA9, NDUFS3, UQCRH, ATP5B
hsa05010Alzheimer's disease12 5.59×10−7COX4I1, NDUFAB1, UQCRC1, NDUFV2, COX6A1, COX6B1, NDUFA4, ATP5C1, NDUFA9, NDUFS3, UQCRH, ATP5B
hsa04310Wnt signaling pathway10 1.70×10−5TCF7L1, PORCN, EP300, PPP2R1A, JUN, CTNNBIP1, NFAT5, TBL1X, RBX1, DAAM1
hsa05131Shigellosis7 1.97×10−5ARPC4, NFKBIA, ELMO1, ACTB, ARPC1A, WASF2, CD44
hsa04520Adherens junction7 4.80×10−5TCF7L1, ACTB, EP300, EGFR, WASF2, FYN, SORBS1
hsa04260Cardiac muscle contraction7 6.66×10−5COX4I1, UQCRC1, COX6A1, COX6B1, ATP1A1, SLC9A6, UQCRH
hsa00020Citrate cycle (TCA cycle)5 7.87×10−5ACLY, OGDHL, MDH2, ACO2, SUCLG1
hsa05120Epithelial cell signaling in Helicobacter pylori infection6 2.87×10−4NFKBIA, ATP6AP1, ATP6V1E1, EGFR, JUN, ATP6V1B2
hsa03050Proteasome5 3.05×10−4PSMD13, PSMB3, PSMC1, PSMC3, PSMD8
hsa05130Pathogenic Escherichia coli infection5 1.11×10−3ARPC4, ACTB, ARPC1A, TUBA1B, FYN
hsa00250Alanine, aspartate and glutamate metabolism4 1.64×10−3NIT2, GAD1, ADSL, GOT1
hsa05100Bacterial invasion of epithelial cells5 3.27×10−3ARPC4, ELMO1, ACTB, ARPC1A, WASF2
hsa04810Regulation of actin cytoskeleton8 4.03×10−3ARPC4, SSH3, MYL12B, ACTB, ARPC1A, EGFR, WASF2, ITGB8

[i] KEGG, Kyoto Encyclopedia of genes and genomes; FDR, false discovery rate; hsa, Homo sapiens.

PPI network construction

The present study established the PPI networks of the top 10 upregulated and downregulated DEGs using Cytoscape software. The interaction network included 863 nodes and 1,304 edges. In the PPI network, degrees of interaction were defined to determine the number of neighbors a node directly connected to, and nodes with a high degree of interaction were defined as hub proteins. The significant hub proteins included β-actin (ACTB; degree, 268), hepatoma-derived growth factor (HDGF; degree, 218) and WD repeat-containing protein 82 (WDR82; degree, 87) (Fig. 3).

Discussion

The present study aimed to identify altered hippocampus gene expression and their further association with other biological processes that regulate causative factors for AD to provide diagnostic factors or therapeutic targets of AD. An integrated analysis of DEGs from four publicly available GEO datasets of hippocampi from patients with AD was performed. In total, 295 genes were consistently differentially expressed across the studies with 109 upregulated genes and 186 downregulated genes. The upregulated gene with the lowest P-value was ZFR, which is mainly expressed in neural tissue (25,26) and may therefore have a role in neuronal function. A recent study identified ZFR as a putative gene associated with hereditary spastic paraplegias by using whole-exome sequencing (27), and from this, the present study deduced that ZFR may be implicated in the underlying processes of AD, which is required to be confirmed by further experiments. The downregulated gene with the lowest P-value was COPG1, whose function remains to be elucidated.

In line with previous studies, certain genes identified in the present study have been closely associated with the development of AD, including S100A6 and TGFBR3. A study on the roles of S100 family proteins in nervous system function and disease found that mRNA expression levels of six family members (S100A1, S100B, S100A6, S100A10, S100A4, S100A13) displayed a 100-fold range in mouse brains, five of which (S1100A1, S100A6, S100A10, S100A13, and S100B) showed age-dependent increases in adult mice that ranged from 5- to 20-fold (28). S100A6-protein immunoreactivity was found to be specifically located within astrocytes associated to amyloid plaques in an APP/London transgenic mouse model of AD, as well as in the brains of patients with AD. S100A6 was upregulated in the amygdala as well as in hippocampal regions (29). Another study detected that biglycan proteoglycans were upregulated in familial AD, while TGFBR3 was markedly downregulated in sporadic AD fibroblasts. Furthermore, the differential expression of TGFBR3 in familial AD and sporadic AD cells was associated with the severity of AD (30).

In the present study,the results of the PPI network analysis of the top 10 upregulated and downregulated DEGs indicated that the significant hub proteins included ACTB, HDGF and WDR82. ACTB, which encodes β-actin, is a candidate reference gene for normalization of target gene expression in polymerase chain reaction (PCR) analysis due to its high conservation. A previous study determined the mRNA levels of ACTB and other genes in the frontal cortex of patients with AD and control subjects using PCR analysis with SYBR Green technology to identify suitable endogenous reference genes in human post-mortem brain tissues for the expression analysis of potential candidate genes associated with AD (31); according to this study, ACTB was the least suitable candidate with reliable expression among a set of suitable endogenous reference genes due to low expression stability in the frontal cortex of AD (32). Of note, the actin cytoskeleton has been reported to have an important role in AD pathology by mediating synaptic degeneration (32).

In order to elucidate the biological roles of the DEGs in AD, a categorized GO enrichment analysis was performed in the present study. The results showed that the respiratory electron transport chain was the most significantly enriched GO category for biological processes. To further evaluate the biological role for the DEGs, the present study performed a KEGG pathway enrichment analysis. According to the KEGG analysis, the most significantly enriched pathway was oxidative phosphorylation. A previous study provided evidence of neuronal metabolic impairments at the transcriptomic and protein level in the brains of patients with AD (33), which was ascribed to the downregulation of mitochondria-associated genes, in particular, oxidative phosphorylation genes in consistency with the fact that AD is a degenerative disease. Furthermore, the present study found that the pathways of several neurodegenerative diseases, including Parkinson's disease, Huntington's disease and Alzheimer's disease, were also highly enriched according to the KEGG pathway enrichment analysis, which was due to dysregulation of genes associated with mitochondrial energy metabolism, including COX4I1, NDUFAB1, UQCRC1, NDUFV2, COX6A1, COX6B1. This finding validated the integrated analysis methods used in the present study.

It is noteworthy that the present study had several limitations. The heterogeneity of the datasets used may have distorted the analysis, as clinical samples may have been heterogeneous with regard to clinical activity or gender. Furthermore, the effects of varying degrees of severity of AD on the differences in hippocampal gene expression were not taken into account. However, the present integrated analysis of different datasets of hippocampal gene expression in patients with AD may have facilitated the detection of genes that would have been missed in the analysis of a single patient or study cohort. Despite these limitations, the present study provided novel information regarding the molecular mechanisms of AD; however, further analyses are required to confirm the present findings.

In conclusion, the present study performed an integrated analysis, which provided significant insight into the global molecular changes associated with AD pathology. Furthermore, the present study identified DEGs as well as other biological functions, which may contribute to the successful identification of diagnostic factors or therapeutic targets for AD and the development of effective targeted therapies. Further functional studies may provide additional insight into the role of the DEGs in the pathophysiology of AD.

References

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

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November-2015
Volume 12 Issue 5

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Spandidos Publications style
Hu W, Lin X and Chen K: Integrated analysis of differential gene expression profiles in hippocampi to identify candidate genes involved in Alzheimer's disease. Mol Med Rep 12: 6679-6687, 2015
APA
Hu, W., Lin, X., & Chen, K. (2015). Integrated analysis of differential gene expression profiles in hippocampi to identify candidate genes involved in Alzheimer's disease. Molecular Medicine Reports, 12, 6679-6687. https://doi.org/10.3892/mmr.2015.4271
MLA
Hu, W., Lin, X., Chen, K."Integrated analysis of differential gene expression profiles in hippocampi to identify candidate genes involved in Alzheimer's disease". Molecular Medicine Reports 12.5 (2015): 6679-6687.
Chicago
Hu, W., Lin, X., Chen, K."Integrated analysis of differential gene expression profiles in hippocampi to identify candidate genes involved in Alzheimer's disease". Molecular Medicine Reports 12, no. 5 (2015): 6679-6687. https://doi.org/10.3892/mmr.2015.4271