Skip to main content
NCBI home page
American Journal of Alzheimer's Disease and Other Dementias logoLink to American Journal of Alzheimer's Disease and Other Dementias
. 2014 Jun 24;30(8):746–751. doi: 10.1177/1533317514537548

Analysis of Differentially Expressed Genes Associated With Alzheimer’s Disease Based on Bioinformatics Methods

Bo Feng 1,2, Peng Hu 3, Jinbo Chen 2, Qingxin Liu 2, Xizhi Li 2, Yifeng Du 1,✉
PMCID: PMC10852745  PMID: 24965283

Abstract

Objective:

To screen differentially expressed genes (DEGs) of Alzheimer’s disease (AD).

Methods:

The gene expression profile (GSE26972) of AD was downloaded from Gene Expression Omnibus database. The DEGs were mapped to protein–protein interaction (PPI) data for acquiring the potential PPI relationship. The coexpressed significance of a gene pair in AD was determined. Then significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs were analyzed based on database for annotation visualization and integrated discovery tool.

Results:

The PPI network showed 7 upregulated genes and 4 downregulated genes that might play meaningful functional roles in AD. Meanwhile, 3 significantly enriched KEGG pathways as well as several significant GO terms (included α-actinin binding, interleukin 33 receptor activity, and telethonin binding) were identified.

Conclusions:

The screened DEGs have the potential to become candidate target molecules to monitor, diagnose, and treat AD.

Keywords: Alzheimer’s disease, differentially expressed genes, protein–protein interaction network, functional enrichment analysis

Introduction

Alzheimer’s disease (AD) was first described 1 by German neurologist Alois Alzheimerin in 1907. As one of the most common types of dementia, AD had characteristics such as progressive memory impairment, cognitive dysfunction, personality changes, language barriers, and other neuropsychiatric symptoms. 2 With the current increasing disease rate of AD, previous studies indicate the total number of people with AD dementia in 2050 to be 13.8 million. 3 Nowadays, clinical diagnostic criteria for AD, especially early diagnosis, are not sufficient. Besides, studies on pathogenesis of AD are still unclear, especially the genetic mechanisms of AD.

Considering the huge damage of AD, preventive measures are urgently needed. In the past decades, research of AD mostly focused on the molecular mechanism. There are 5 main pathogenesis theories about AD including cerebral accumulation of amyloid β (Aβ) protein, 4 central nervous cholinergic damage, 5 excitatory amino acids, 6 abnormal phosphorylation of τ protein, 7 and dysregulation of intracellular calcium. 8,9 Moreover, many genes have proved the association with AD. Amyloid protein precursor (APP), 10 presenilin 1 (ps-1), 11 and presenilin-2 (ps-2) 12 have been confirmed as virulence gene of familial AD, while apolipoprotein E (apoE) 13 has been proved tightly relevant to sporadic AD. Meanwhile, the mutation of APP is proved to cause an amino acid substitution of the carboxy terminus of the β-amyloid (Aβ) peptide. 10 Additionally, mutant PS1 influences APP processing both in vitro and in vivo, and the elevated extracellular concentrations of amyloidogenic Aβ1-42 peptides are precipitated disease in PS1-linked familial AD. 14 Furthermore, apoE is now considered to have best-established genetic association with AD, 15 especially the apoE ∊4 which is considered as the only well-verified susceptibility gene. The effect of apoE ∊4 on AD is influenced by age and ethnicity. 16 Although previous studies have identified several potential genes and proteins as determinants of AD, the needs for more research to elucidate the mechanism of AD are highlighted.

In this article, differentially expressed genes (DEGs) between AD samples and normal samples were identified based on the data downloaded from Gene Expression Omnibus (GEO) database. Bioinformatics methods were used to construct a protein–protein interaction (PPI) network of DEGs. Meanwhile, function and pathway annotation of DEGs were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways databases. The present study hoped to provide a new view and evidence for the mechanism of AD.

Materials and Methods

Derivation of Genetic Data

Gene expression profile (GSE26972) 17 was downloaded from a public functional genomics data repository GEO (http://www.ncbi.nlm.nih.gov/geo/) database, including 3 AD samples and 3 normal samples. Those data were analyzed based on Affymetrix Human Exton 1 S.T arrays.

Data Preprocessing

The raw genetic data were analyzed by the Oligo package on R-bioconductor, (http://www.bioconductor.org/). 18 while the normalization and calculation of expression value were performed by robust multiarray average algorithm. 19 was utilized for background correction, normalization, and calculation of expression value.

The t test was used to identify genes that were significantly differentially expressed between 3 AD samples and 3 normal samples. Then, the DEGs with fold change value of >2 or <0.5 and P value of <.05 were only selected. After that, cluster analysis was performed to guarantee the screened DEGs.

Function Annotation of DEGs

Gene Ontology function and enrichment analysis of KEGG pathways were performed based on database for annotation visualization and integrated discovery (DAVID) 20 online analytical tools. The GO terms and KEGG pathways with P value less than .05 were identified.

Construction of PPI Network

Ingenuity Pathway Analysis (IPA) database was used to obtain information about the PPI and protein–biomolecule interaction. After the PPI network construction based on the IPA database, the DEGs were mapped to the PPI data that have been collected from mint 21 and hprd 22 database. To determine the coexpressed significance of a gene pair in AD, Pearson’s correlation coefficient (PCC) 23 test was used to calculate the relationship between nodes and edges in the PPI network. Finally the PPI network of AD with the |PCC| >0.6 and each edge linked with at least 1 DEG was constructed.

Results

Analysis of DEGs

A total of 12 DEGs were identified (Figure 1). Clusters analysis of DEGs showed that the upregulated DEGs included dentin matrix protein 2 (DMP2), hormonally upregulated Neu-associated kinase (HUNK), neuropilin and tolloid-like 1 (NETO1), and solute carrier family 24, member 3 (SLC24A3), while the downregulated DEGs included complement component (3b/4b) receptor 1 (CR1), EF-hand calcium binding domain 6 (EFCAB6), glutathione S-transferase theta 1 (GSTT1), interleukin 1 receptor-like 1 (IL1RL1), olfactory receptor, family 7, subfamily A, member 5 (OR7A5), palladin, cytoskeletal associated protein (PALLD), tripartite motif containing 5 (TRIM5), and titin (TTN).

Figure 1.

Figure 1.

Open in a new tab

Dendrogram of differentially expressed genes (DEGs) by cluster analysis. The below abscissa axis represents specimen: NDC represents normal sample, AD represents sample of Alzheimer’s disease. The above abscissa axis represents the clustering of specimens. The right longitudinal axis represents genes. Red shows upregulated genes while green shows downregulated genes.

Construction of PPI Network

The PPI network was constructed based on IPA database (Figure 2A). To further investigate the potential function of significant DEGs and their related proteins, the nodes and lines of top 5 DEGs were abscised (Figure 2B). The nodes of top 5 DEGs were ubiquitin C (UBC), β-estradiol, APP, interferon gamma (IFNG), and Ca2+. Moreover, 8 upregulated genes including TTN, CR1, IL1RL1, EFCAB6, PALLD, OR7A5, GSTT1, and TRIM5 as well as 4 downregulated genes including HUNK, DMP2, SLC24A3, and NETO1 were revealed in this PPI network.

Figure 2.

Figure 2.

Open in a new tab

Protein–protein interaction (PPI) network of different expression genes (DEGs) based on Information-technology Promotion Agency Japan (IPA) database. A, PPI network of DEGs based on IPA database. B, PPI network after nodes and lines of top 5 were abscised. The red points stand for upregulated genes, the green points stand for downregulated genes, and the white points stand for genes which have PPI with DEGs. The size of node corresponds to the degree of this node. The thickness of the lines represents the ligation: the thicker the line is, the more important this connection is.

Function and Pathway Enrichment Analysis of DEGs

Gene Ontology functional enrichment indicated that DEGs were significantly enriched into several GO terms, such as α-actinin binding, interleukin 33 receptor activity, and telethonin binding. Top 10 of GO terms are listed in Table 1. Besides, the result of KEGG analysis revealed 3 enriched KEGG pathways: glycosylphosphatidylinositol (GPI)-anchor biosynthesis, N-glycan biosynthesis, and glutathione metabolism (Table 2).

Table 1.

Significantly Enriched Gene Ontology Terms of Differentially Expression Genes (Top 10).a

GO ID P Value Count Term
GOTERM_BP_ALL
 GO:0031033 .002108 1 Myosin filament assembly or disassembly
 GO:0018406 .002108 1 Protein amino acid C-linked glycosylation via 2'-α-mannosyl-L-tryptophan
 GO:0018103 .002108 1 Protein amino acid C-linked glycosylation
 GO:0018211 .002108 1 Peptidyl-tryptophan modification
 GO:0035269 .002108 1 Protein amino acid O-linked mannosylation
 GO:0030241 .002108 1 Muscle thick filament assembly
 GO:0048739 .00281 1 Cardiac muscle fiber development
 GO:0030240 .003511 1 Muscle thin filament assembly
 GO:0055003 .004212 1 Cardiac myofibril assembly
 GO:0045087 .005142 2 Innate immune response
GOTERM_MF_ALL
 GO:0051393 .000721 1 α-actinin binding
 GO:0002114 .000786 1 Interleukin 33 receptor activity
 GO:0031433 .000786 1 Telethonin binding
 GO:0008273 .001572 1 Calcium, potassium–sodium antiporter activity
 GO:0051371 .001572 1 Muscle α-actinin binding
 GO:0004875 .002357 1 Complement receptor activity
 GO:0004582 .003141 1 Dolichyl-phosphate β-d-mannosyltransferase activity
 GO:0004908 .005491 1 Interleukin 1 receptor activity
 GO:0043621 .007836 1 Protein self-association
 GO:0015491 .013288 1 Cation–cation antiporter activity
GOTERM_CC_ALL
 GO:0031501 .002058 1 Mannosyltransferase complex
 GO:0000506 .002058 1 Glycosylphosphatidylinositol-N-acetylglucosaminyltransferase (GPI-GnT) complex
 GO:0000932 .015004 1 Cytoplasmic mRNA processing body
 GO:0005884 .019735 1 Actin filament
 GO:0030018 .023101 1 Z disk
 GO:0000794 .025116 1 Condensed nuclear chromosome
 GO:0030176 .027797 1 Integral to endoplasmic reticulum membrane
Open in a new tab

Abbreviations: GO, Gene Ontology; mRNA, messenger RNA.

a P value less than .05 were consider to be significantly different.

Table 2.

The Enriched KEGG Pathways.

KEGG ID P Value Count Term
563 .024489 1 Glycosylphosphatidylinositol (GPI)-anchor biosynthesis
510 .042779 1 N-Glycan biosynthesis
480 .048497 1 Glutathione metabolism
Open in a new tab

Abbreviation: KEGG, Kyoto Encyclopedia of Genes and Genomes.

Discussions

In the present study, a gene expression profile downloaded from GEO was used to explore the possible candidate genes of AD. A total of 12 DEGs (4 upregulated genes and 8 downregulated genes) were identified between the normal samples and samples with AD.

Protein–protein interaction network showed that TTN, CR1, IL1RL1, EFCAB6, PALLD, OR7A5, GSTT1, and TRIM5 were upregulated genes and HUNK, DMP2, SLC24A3, and NETO1 were downregulated genes. As a kind of protein degradation pathway, 24 ubiquitin proteasome pathway (UPP) dysregulation could induce the overphosphorylation of τ and degradation dysfunction of Aβ in AD. 25,26 The TRIM proteins consist of an N-terminal E3 ubiquitin ligase Really Interesting New Gene domain which has been demonstrated to possess E3 ubiquitin ligase activity in vitro allowing self-polyubiquitylation 27 and played roles in the polyubiquitylation and turnover of the protein. 28 The present study showed that TRIM5 was overexpressed in samples with AD group which inferred that the overexpression of TRIM5 might activate the UPP and increase decomposition of paraprotein. Besides, as TTN is reported to modulate Ca2+ homeostasis, 29 and Ca2+ is an important factor in AD, 30 there is no surprise that TTN is differently changed in AD. Moreover, previous study indicates that NETO1 is associated with neurological diseases by the effects on the maintenance of neural circuitry and synaptic plasticity. 31 Drugs that bind to or modulate the activity of protein encoded by PALLD gene are suggested for the treatment of AD. 32 Genetic dysregulation of the IL1RL1 axis appeared to be involved in conferring predisposition to AD, 33 and the IL1RL1 pathway plays an important role in AD. 34 Consistent with the above-mentioned studies, our results indicated that NETO1, PALLD, and IL1RL1 might be involved in AD. Furthermore, an association between AD risk and markers spanning CR1 has been observed, 35 and human erythrocytes, which abundantly expressed CR1, was able to sequester Aβ 36,37 and to favor its clearance via the C3b-mediated adherence to erythrocyte CR1. 37 In a word, CR1 played a protective role via the generation and binding of C3b, which might contribute to Aβ clearance. 38 Therefore, we speculated that CR1 might play a role in AD via effecting on Aβ. There was no literature drawn that EFCAB6 and OR7A5 were related to AD. Thus, the present study might add a new candidate to the list of genes involved in AD. The PPI network also showed that β-estradiol might enhance the expression of DMP2, HUNK, and scl24A3 and decrease the expression of GSTT1. Figure 1 shows DMP2, HUNK, and SLC24A3 are low expressed, and GSTT1 is overexpressed in samples with AD. Previous studies indicate that the expression of HUNK may be regulated by β-estradiol, 39 and GSTT1 is involved in estrogen metabolism. 40 The changed expression of DMP2, HUNK, scl24A3, GSTT1, and β-estradiol might result in the interactions between β-estradiol and these genes, which needed further studies. The PPI network result also showed that UBC, 41 β-estradiol, 10 APP, 42 IFNG, 43 and Ca2+ were30 important factors in AD, which have been reported to have important relevance with the pathogenesis of AD. These 5 factors might be involved in the 5 pathogenesis theories of AD which was mentioned earlier. 4 –9 Besides, β-estradiol has already been used as a potential therapeutic drug for AD, 44 while APP has been considered as the criteria for the diagnosis of AD. 45 This study indicated that DEGs that were related to UBC, IFNG, and Ca2+ pathways might be the important potential therapeutic targets for AD.

Additionally, the analysis of KEGG in the present study revealed 3 enriched pathways: GPI-anchor biosynthesis, N-glycan biosynthesis, and glutathione metabolism. Amyloid β, which is thought to be one of the causes of AD, is a cleaved fragment from N-glycans of amyloid precursor protein, and the number of N-glycans having a bisecting GlcNAc residue is proved in AD brains. 46 Considering the above-mentioned information, we hypothesized that N-glycan biosynthesis might be involved in the pathogenesis of AD. Besides, research that focused on glutathione metabolism demonstrated the induction of glutathione reductase and glutathione peroxidase messages in the AD hippocampus 47 and showed that decreased glutathione content might be involved in AD pathology in humans. 48 However, there is no evidence to support that GPI-anchor biosynthesis is involved in AD.

In conclusion, the DEGs of AD were analyzed by computational bioinformatics approaches. The present study indicated that TTN, CR1, IL1RL1, EFCAB6, PALLD, OR7A5, and GSTT1 were upregulated genes and HUNK, DMP2, SLC24A3, NETO1 were downregulated genes that might play meaningful roles in AD. Meanwhile, the enriched pathway revealed several pathways including GPI-anchor biosynthesis pathway, N-glycan biosynthesis pathway, and glutathione metabolism pathway. The present study indicated several potential target molecules to treat the AD and might shed new light on the mechanism of AD.

Acknowledgments

This work was supported by Science and technology project of Binzhou medical university and Science and technology project of Colleges and universities in Shandong Province.

Footnotes

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Science and technology project of Binzhou medical university and Science and technology project of Colleges and universities in Shandong Province

References

  • 1. Windaus A, Vogt W. Synthese des Imidazolyl-äthylamins. Berichte der deutschen chemischen Gesellschaft. 1907;40 (3):3691–3695. [Google Scholar]
  • 2. Morris RG, Baddeley AD. Primary and working memory functioning in Alzheimer-type dementia. J Clin Exp Neuropsychol. 1988;10 (2):279–296. [DOI] [PubMed] [Google Scholar]
  • 3. Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology. 2013;80 (19):1778–1783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Selkoe DJ. Toward a comprehensive theory for Alzheimer’s disease. Hypothesis: Alzheimer’s disease is caused by the cerebral accumulation and cytotoxicity of amyloid beta-protein. Ann N Y Acad Sci. 2000;924:17–25. [DOI] [PubMed] [Google Scholar]
  • 5. Fisher A. Cholinergic treatments with emphasis on m1 muscarinic agonists as potential disease-modifying agents for Alzheimer’s disease. Neurotherapeutics. 2008;5 (3):433–442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Csernansky JG, Bardgett ME, Sheline YI, Morris JC, Olney JW. CSF excitatory amino acids and severity of illness in Alzheimer’s disease. Neurology. 1996;46 (6):1715–1720. [DOI] [PubMed] [Google Scholar]
  • 7. Goedert M, Jakes R, Crowther RA, et al. The abnormal phosphorylation of tau protein at Ser-202 in Alzheimer disease recapitulates phosphorylation during development. Proc Natl Acad Sci U S A. 1993;90 (11):5066–5070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Supnet C, Bezprozvanny I. The dysregulation of intracellular calcium in Alzheimer disease. Cell Calcium. 2010;47 (2):183–189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Berridge MJ. Calcium regulation of neural rhythms, memory and Alzheimer’s disease. J Physiol. 2014;592 (pt 2):281–293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Goate A, Chartier-Harlin MC, Mullan M, et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature. 1991;349 (6311):704–706. [DOI] [PubMed] [Google Scholar]
  • 11. Veeraraghavalu K, Choi SH, Sisodia SS. Expression of familial Alzheimer’s disease-linked human presenilin 1 variants impair enrichment-induced adult hippocampal neurogenesis. Neurodegener Dis. 2010;7 (1-3):46–49. [DOI] [PubMed] [Google Scholar]
  • 12. Jayadev S, Leverenz JB, Steinbart E, et al. Alzheimer’s disease phenotypes and genotypes associated with mutations in presenilin 2. Brain Res. 2010;133 (pt 4):1143–1154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Saunders AM, Strittmatter WJ, Schmechel D, et al. Association of apolipoprotein E allele ∊4 with late-onset familial and sporadic Alzheimer’s disease. Neurology. 2011;77:950–950. [DOI] [PubMed] [Google Scholar]
  • 14. Borchelt DR, Thinakaran G, Eckman CB, et al. Familial Alzheimer’s disease-linked presenilin 1 variants elevate Abeta1-42/1-40 ratio in vitro and in vivo. Neuron. 1996;17 (5):1005–1013. [DOI] [PubMed] [Google Scholar]
  • 15. Papassotiropoulos A, Fountoulakis M, Dunckley T, Stephan DA, Reiman EM. Genetics, transcriptomics, and proteomics of Alzheimer’s disease. J Clin Psychiatry. 2006;67 (4):652–670. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Saunders AM, Strittmatter WJ, Schmechel D, et al. Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer’s disease. Neurology. 1993;43 (8):1467–1472. [DOI] [PubMed] [Google Scholar]
  • 17. Berson A, Barbash S, Shaltiel G, et al. Cholinergic-associated loss of hnRNP-A/B in Alzheimer’s disease impairs cortical splicing and cognitive function in mice. EMBO Mol Med. 2012;4 (8):730–742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Toedling J, Skylar O, Krueger T, Fischer JJ, Sperling S, Huber W. Ringo-an R/Bioconductor package for analyzing ChIP-chip readouts. BMC Bioinformatics. 2007;8:221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4 (2):249–264. [DOI] [PubMed] [Google Scholar]
  • 20. Dennis G, Jr, Sherman BT, Hosack DA, et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4 (5):P3. [PubMed] [Google Scholar]
  • 21. Licata L, Briganti L, Peluso D, et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2012;40 (database issue):D857–D861. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Keshava Prasad TS, Goel R, Kandasamy K, et al. Human protein reference database--2009 update. Nucleic Acids Res. 2009;37 (database issue):D767–D772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Pietrokovski S. Searching databases of conserved sequence regions by aligning protein multiple-alignments. Nucleic Acids Res. 1996;24 (19):3836–3845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Tanaka K. Molecular biology of proteasome. Mol Biol Rep. 1995;21 (1):21–26. [DOI] [PubMed] [Google Scholar]
  • 25. Cripps D, Thomas SN, Jeng Y, Yang F, Davies P, Yang AJ. Alzheimer disease-specific conformation of hyperphosphorylated paired helical filament-Tau is polyubiquitinated through Lys-48, Lys-11, and Lys-6 ubiquitin conjugation. J Biol Chem. 2006;281 (16):10825–10838. [DOI] [PubMed] [Google Scholar]
  • 26. Song S, Kim SY, Hong YM, et al. Essential role of E2-25K/Hip-2 in mediating amyloid-β neurotoxicity. Molecular Cell. 2003;12 (3):553–563. [DOI] [PubMed] [Google Scholar]
  • 27. Lorick KL, Jensen JP, Fang S, Ong AM, Hatakeyama S, Weissman AM. RING fingers mediate ubiquitin-conjugating enzyme (E2)-dependent ubiquitination. Proc Natl Acad Sci. 1999;96 (20):11364–11369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Diaz-Griffero F, Li X, Javanbakht H, et al. Rapid turnover and polyubiquitylation of the retroviral restriction factor TRIM5. Virology. 2006;349 (2):300–315. [DOI] [PubMed] [Google Scholar]
  • 29. Yamasaki R, Berri M, Wu Y, et al. Titin-actin interaction in mouse myocardium: passive tension modulation and its regulation by calcium/S100A1. Biophys J. 2001;81 (4):2297–2313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Matsushima H, Shimohama S, Chachin M, Taniguchi T, Kimura J. Ca2+-Dependent and Ca2+-independent protein kinase C changes in the brains of patients with Alzheimer’s disease. J Neurochem. 1996;67 (1):317–323. [DOI] [PubMed] [Google Scholar]
  • 31. Walter S, Atzmon G, Demerath EW, et al. A genome-wide association study of aging. Neurobiol Aging. 2011;32 (11):2109. e2115–e2128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Chumakov I CD, Graudens E, et al. , inventor New therapeutic approaches for treating alzheimer disease and related disorders through a modulation of angiogenesis.2009. [Google Scholar]
  • 33. Latiano A, Palmieri O, Pastorelli L, et al. Associations between genetic polymorphisms in IL-33, IL1R1 and risk for inflammatory bowel disease. PloS One. 2013;8 (4):e62144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Oboki K, Ohno T, Kajiwara N, Saito H, Nakae S. IL-33 and IL-33 receptors in host defense and diseases. Allergol Int. 2010;59 (2):143–160. [DOI] [PubMed] [Google Scholar]
  • 35. Lambert JC, Heath S, Even G, et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet. 2009;41 (10):1094–1099. [DOI] [PubMed] [Google Scholar]
  • 36. Kuo YM, Kokjohn TA, Kalback W, et al. Amyloid-beta peptides interact with plasma proteins and erythrocytes: implications for their quantitation in plasma. Biochem Biophys Res Commun. 2000;268 (3):750–756. [DOI] [PubMed] [Google Scholar]
  • 37. Zhou J, Fonseca MI, Pisalyaput K, Tenner AJ. Complement C3 and C4 expression in C1q sufficient and deficient mouse models of Alzheimer’s disease. J Neurochem. 2008;106 (5):2080–2092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Wyss-Coray T, Yan F, Lin AH, et al. Prominent neurodegeneration and increased plaque formation in complement-inhibited Alzheimer’s mice. Proc Natl Acad Sci U S A. 2002;99 (16):10837–10842. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Gardner HP, Belka GK, Wertheim GB, et al. Developmental role of the SNF1-related kinase Hunk in pregnancy-induced changes in the mammary gland. Development. 2000;127 (20):4493–4509. [DOI] [PubMed] [Google Scholar]
  • 40. Mitrunen K, Kataja V, Eskelinen M, et al. Combined COMT and GST genotypes and hormone replacement therapy associated breast cancer risk. Pharmacogenetics. 2002;12 (1):67–72. [DOI] [PubMed] [Google Scholar]
  • 41. Zhang Y, Tounekti O, Akerman B, Goodyer CG, LeBlanc A. 17-beta-estradiol induces an inhibitor of active caspases. J Neurosci. 2001;21 (20):RC176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Scheuner D, Eckman C, Jensen M, et al. Secreted amyloid −protein similar to that in the senile plaques of Alzheimer’s disease is increased in vivo by the presenilin 1 and 2 and APP mutations linked to familial Alzheimer’s disease. Nat Med. 1996;2 (8):864–870. [DOI] [PubMed] [Google Scholar]
  • 43. Rota E, Bellone G, Rocca P, Bergamasco B, Emanuelli G, Ferrero P. Increased intrathecal TGF-β1, but not IL-12, IFN-γ and IL-10 levels in Alzheimer’s disease patients. Neurol Sci. 2006;27 (1):33–39. [DOI] [PubMed] [Google Scholar]
  • 44. Kumar P, Kale R, Baquer N. Mechanisms for the protective effects of 17-beta-estradiol: a therapeutic potential drug for Alzheimer’s disease. Cytotherapy. 2013;15:S34–S34. [Google Scholar]
  • 45. Waldemar G, Dubois B, Emre M, et al. Recommendations for the diagnosis and management of Alzheimer’s disease and other disorders associated with dementia: EFNS guideline. Eur J Neurol. 2007;14 (1):e1–e26. [DOI] [PubMed] [Google Scholar]
  • 46. Akasaka-Manya K, Manya H, Sakurai Y, et al. Protective effect of N-glycan bisecting GlcNAc residues on beta-amyloid production in Alzheimer’s disease. Glycobiology. 2010;20 (1):99–106. [DOI] [PubMed] [Google Scholar]
  • 47. Aksenov MY, Markesbery WR. Changes in thiol content and expression of glutathione redox system genes in the hippocampus and cerebellum in Alzheimer’s disease. Neurosci Lett. 2001;302 (2-3):141–145. [DOI] [PubMed] [Google Scholar]
  • 48. Liu H, Wang H, Shenvi S, Hagen TM, Liu RM. Glutathione metabolism during aging and in Alzheimer disease. Ann N Y Acad Sci. 2004;1019:346–349. [DOI] [PubMed] [Google Scholar]

Articles from American Journal of Alzheimer's Disease and Other Dementias are provided here courtesy of SAGE Publications

RESOURCES