Computational Molecular Biology 2016, Vol.6, No.2, 1-9
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binding of cell surface ApoE receptors (Chen et al., 2011). Human ApoE exists as three polymorphic alleles: ε2,
ε3 and ε4 (Frieden and Garai, 2012). These three allele forms differ from each other in single amino acid change,
resulting in different protein structures, lipid association and receptor binding (Zhong and Weisgraber, 2006; Bu,
2006; Corder et al., 1993). The ε4 allele of the ApoE is the strongest genetic risk factor for late-onset AD (LOAD)
(Farrer et al., 1997). Individuals with one ε4 allele are 3–4 times more likely to develop AD than those without ε4
allele (Laws et al., 2003). Interestingly, the rare ε2 allele has a protective effect against AD compared with the ε3
allele (Taddei et al., 1997). Studying the regulatory elements of disease genes and their corresponding
transcription factors is therefore critically important for elucidation of the disease processes (Mahley, 1988). This
review will discuss the mechanisms of transcriptional regulation for AD genes, and the misregulation that leads to
AD susceptibility. A large number of genes that associated with disease, although there is a need to identify
expression levels of genes, etiology and gene regulations and much functions remains unknown.
2 Materials and Methods
2.1 Data selection
The whole genome analysis of Alzheimer’s disease (AD) to identify the copy number variation and gene
mutations. The histopathology of disease shows extracellular amyloid plaques and intracellular neurofibrillary
tangles. The histopathology and pathophysiology of disease helps to identify the disease prognosis. There is a
search for biomarkers of Alzheimer’s disease (AD) have yielded numerous expensive and/or invasive candidates,
including putative disease markers. To assess the state of blood-based biomarkers, genetic markers and
inflammatory markers for AD including the understanding the context of use to begin outlining the research
challenges related to the development of markers including understanding the context of use from a clinical,
research, and regulatory perspective; highlighting the need for standardization and harmonization of protocols;
and identifying knowledge gaps and the research efforts needed to fill those gaps.
2.2 Microarray analysis
The systematic identification and characterization of Alzheimer’s disease (AD) specific transcriptome cerebral
cortex gene analysis using microarray gene expression data, the dataset contains 31 samples of which 9 control
and 22 AD affected samples obtained from Gene Expression Omnibus (GEO Accession Number: GDS1297) was
used in this study. Gene expression was measured using GPL96 annotation file to compare the covering 22283
genes for AD (22 samples) and control (9 samples). The gene expression of control and AD stage has been
considered and compared for the identification of differentially expressed genes in AD stage. Significance analysis
of Microarray (SAM) determines the significant changes in expression of genes between different biological
stages based on statistical analysis of modified gene specific t-test. We used MultiExperiment Viewer (MEV)
software package from TIGR for hierarchical clustering of microarray data, using Euclidean Distance metrics and
Average Linkage Clustering algorithms. The Tree View shows the relationship between the genes based on the
gene expression profile.
2.3 Analysis of gene enrichment for transcriptome cerebral cortex genes
Each clusters of gene set was analyzed for enrichment of transcriptome cerebral cortex genes using the DAVID
Database. The conserved non-coding regions of the promoters were searched for matches to all cerebral cortex
profiles in the GOrilla database.
3 Results
The identification of blood, inflammatory and genetic markers involved in diagnosis of Alzheimer's disease in
early state using transcriptome specific cerebral cortex brain tissue to identify the differential expressed genes,
that acts as a potential markers. The selected dataset contains 22283 genes of 31 samples of which 9 controls and
22 disease samples. All the samples are classified into four groups based on pathogenesis, such as control 9,
incipient 7, moderate 8, and severe 7. The selected samples are tested with two different conditions based on
diagnostic tests such as MiniMental Status Examination (MMSE) and neurofibrillary tangle (NFT) scores across
all 31 subjects regardless of diagnosis (Table 1). The quality control of dataset is the major concept of design and
