Send Orders for Reprints to reprints@benthamscience.net CNS & Neurological Disorders - Drug Targets, 2014, 13, 501-516 501 Establishing Genomic/Transcriptomic Links Between Alzheimer’s Disease and Type 2 Diabetes Mellitus by Meta-Analysis Approach Zeenat Mirza 1 , Mohammad A. Kamal 1 , Adel M. buzenadah 1,2 , Mohammed H. Al-Qahtani 2 and Sajjad Karim *,2 1 King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia 2 Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia Abstract: Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta- analysis methods. Using cDNA microarray technology (Partek Genomics Suite 6.6) and global pathway analysis with Ingenuity Pathway Analysis tool (IPA, Inc), we examined the transcript level in type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (AD) patients and controls. To understand the molecular link between T2DM and AD, we compared the gene expression pattern and pathway involved. Microarray analysis identified 235 differentially expressed genes between T2DM patients and controls; and 834 between AD and controls at two fold change and a false discovery rate of 0.05. Significantly changed expression of “myeloid leukemia cell differentiation protein 1; RAS guanyl releasing protein 1; S100 calcium-binding protein A8; prostaglandin- endoperoxide synthase 2; parvalbumin; endoplasmic reticulum aminopeptidase 1; phosphoglycerate kinase 1; Eukaryotic translation initiation factor 3 subunit F; Interleukin-1 beta; tubulin, beta 2A; glycine receptor alpha 1 and ribosomal protein S24” genes were highly associated with T2DM, whereas “neuronal differentiation 6; G-protein coupled receptor 83; phosphoserine phosphatase; bobby sox homolog or HMG box -containing protein 2; Glutathione S-transferase theta 1; alpha-2-glycoprotein 1 zinc-binding; Heat shock 70kDa protein 1B; transportin 1, Acidic leucine-rich nuclear phosphoprotein 32 family member B; Nuclear factor of activated T-cells 5; inositol 1,4,5-trisphosphate 3-kinase B; prenylcysteine oxidase 1 like” were found to be strongly related with AD. We also found a set of differentially expressed genes; “ARP2 actin-related protein 2; Cell division control protein 42; cytoplasmic polyadenylation element binding protein 4; Early growth response protein 1; ectonucleotide pyrophosphatase/phosphodiesterase 5; folate receptor 1; glutamate-ammonia ligase; hydroxy-3-methylglutaryl-Coenzyme A reductase; 3-hydroxy-3- methylglutaryl-CoA synthase; interleukin 1 receptor- like 1; leukemia inhibitory factor receptor; metastasis associated lung adenocarcinoma transcript 1; pyruvate dehydrogenase kinase, isozyme 4; phosphoserine phosphatase, parvalbumin, and tubulin, beta 2A” to be present in both dataset. Altered regulation of intracellular signaling pathways, including Ephrin receptor, liver X receptor/ retinoid X receptor; interleukin 6; insulin- like growth factor 1; interleukin 10 and 14-3-3-mediated signaling pathways were associated with T2DM as well as Alzheimer-type pathology. Our findings implicate diabetic disorders in the pathogenesis of AD, and provide a basis for future candidate studies based on specific pathways. Keywords: Alzheimer's disease, canonical pathways, meta-analysis, microarray, transcriptomics, type 2 diabetes mellitus. INTRODUCTION Microarray-based expression profiling is a widely used, quick and inexpensive method to obtain information about the specific diseases. In recent years many researchers have embraced microarray technology and there has been an explosion in publicly available datasets. Examples of expression data repositories include Gene Expression Omni- bus (GEO, http://www.ncbi.nlm.nih.gov/geo/), ArrayExpress (http://www.ebi.ac.uk/microarray-as/ae/) and Stanford Mic- roarray Database (http://genome-www5.stanford.edu/) as well as researchers’ and institutions’ websites. The use of these datasets is not exhausted, when used wisely they may reveal *Address correspondence to this author at the Center of Excellence in Genomic Medicine Research, King Abdulaziz University, PO Box-80216, Jeddah, Pin-21589, Kingdom of Saudi Arabia; Tel: +966-557581741, +966-12-6401000, Ext. 25123; E-mail: sajjad_k_2000@yahoo.com; skarim1@kau.edu.sa a depth of new information. Demand has increased to effectively utilise these datasets in current research as additional data for analysis and verification. Meta-analysis refers to an integrative data analysis method that traditionally is defined as a synthesis or at times review of results from datasets that are independent but related [1]. Meta-analysis has ranging benefits. Power can be added to an analysis, obtained by the increase in cohort size of the study. This aids the ability of the analysis to find effects that exist and is termed 'integration-driven discovery' [2]. Meta-analysis can also be important when studies have conflicting conclusions as they may estimate an average effect or highlight an important subtle variation [1, 3]. There are a number of issues associated with applying meta-analysis in gene expression studies. These include problems common to traditional meta-analysis such as overcoming different aims, design and populations of interest. There are also concerns specific to gene expression data including challenges with variable probes and probe 1996-3181/14 $58.00+.00 © 2014 Bentham Science Publishers