Computational Analysis of Transcription Profiling Gene Expression Data of Alzheimer’s Brain Ashwani Kumar and Tiratha Raj Singh* Department of Biotechnology and Bioinformatics Jaypee University of Information Technology Waknaghat, Solan, H.P *tiratharaj.singh@juit.ac.in Introduction The pathogenesis of incipient Alzheimer’s disease (AD) has been difficult to analyse because of the complexity of AD and the overlap of its early-stage markers with normal aging. Genome-wide transcription profiling is a powerful diagnostic technique applied to disease tissue that may reveal quantitative and qualitative alterations in gene expression, that give information about genetics and pathogenesis of disease. This transcription level analysis could provide essential clues regarding expression of genes associated to AD and further reduce the complexity in understanding the involved mechanism. Methodology START Cluster Heatmap are generated according to Hierarchical gene mapping using uncentered correlation distance and complete linkage algorithm. High expression and low expression gene are shown with varied color. Group the sample according to expression value of genes in samples and then ranked according to adj.P value and logFC value. Compare Two or more sets of samples using GEO2R tool contain different genes using two tailed T-test at significance level of 0.01 Hippocampus Gene Expression Data of 9 control and 22 postmortem subjects with AD at various stage of severity whose series id is GSE1297 taken from GEO. Results Figure 3: Cluster Heat Map for Hierarchial cluster Conclusion It has been observed that the over expression data based on low adj.P value and high log fold change value, there was frequent occurrence of major histocampatibilty complex I and complex II related genes, PSAT1 and FGFR2, etc. while for the under expression genes are RAB25 and ATG5 and few transcription factors were also found. From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal functions. Control Incipient AD Moderate AD Severe AD Figure 1: Gene expression changes with number of counts and percentile rank Table 1: GEO2R result show expression of genes are ranked on basis of adj.P .value and log FC value ID Adj.P.Value Log FC Gene Symbol Gene Expression 211911_x_at 0.0037 1.209915 HLA-B High 220892_S_at 0.00337 1.33895 PSAT1 203639_s_at 1.680364 FGFR2 214953_s_at 0.068 0.511453 APP Medium 203460_s_at 0.109 0.440217 PSEN1 203382_s_at 0.13049 -0.5282 APOE 218186_s_at 0.29694 -0.8267 RAB25 Low 210639_s_at 0.27035 ATG5 References Ricciarelli, Roberta, et al. "Microarray analysis in Alzheimer's disease and normal aging." IUBMB life 56.6 (2004): 349-354. Augustin, Regina, et al. "Bioinformatics identification of modules of transcription factor binding sites in Alzheimer's disease-related genes by in silico promoter analysis and microarrays." International Journal of Alzheimer’s Disease 2011 (2011). Kumar, Ashwani, and Singh, T.R.,” Systems biology approach for gene set enrichment and topological analysis of Alzheimer’s disease pathway.,” BSB, International Conference on. IEEE,2016. Verhaak, Roel GW, et al. ”Integral genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.” Cancer cell 17.1 (2010): 98.-110. Discussion These studies reveal that widespread change in genomic regulation of genes in multiple pathways are majorly correlated to AD. Transcriptional microarray analysis provide correlation of many genes through cluster heat mapping and profiling. However major coordination seen in AD may provide a new perspective on the possible origins of these harmful processes in various stage of AD pathogenesis. STOP