Meta analysis of alzheimer’s disease at the gene expression level

Download

index.pdf

Date

2017

Author

İzgi, Hamit

Metadata

Show full item record

Item Usage Stats

427
views

108
downloads

In this study, publicly available microarray gene expression datasets are used to investigate common gene expression changes in different postmortem brain regions in Alzheimer’s Disease (AD) patients compared to control subjects, and to find possible functional associations related to these changes. The hypothesis is that pathogenesis of the disease converges into common patterns of dysregulation/alteration or dysfunction in molecular pathways across different brain regions in AD. In total, I studied 13 datasets, one of which was excluded from the analysis in quality checks, resulting in 12 datasets spanning 7 different brain regions. Instead of using the standard approach to identify differentially expressed genes in each dataset independently, I used an alternative scheme, focusing on shared trends across all datasets, and testing their significance using cross-dataset structured permutations. Among more than 8000 common genes in all 12 datasets, I identified those showing shared upregulated (631) or downregulation (580) trends in AD across all datasets, which was highly significant compared to permutations. I then performed GO Biological Process enrichment analysis on both gene sets. There were 343 GO BP categories enriched for upregulated genes and 94 GO BP categories enriched for downregulated genes. Among 343 GO categories enriched for upregulated genes, the most noticeable ones include protein modification, differentiation, and the cell cycle. Furthermore, cell-cell signaling, synaptic activity and energy metabolism related pathways are enriched in downregulated genes. These findings are in line with the effects of pathological changes in AD and suggests that different brain regions share common pathways deregulated by AD.

Subject Keywords

Brain, Gene expression., Alzheimer's disease.

URI

http://etd.lib.metu.edu.tr/upload/12620729/index.pdf
https://hdl.handle.net/11511/26240

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Transcriptomic network analysis of brain aging and alzheimers disease Parvizi, Poorya; Somel, Mehmet; Tunçbağ, Nurcan; Department of Biology (2017) Multiple studies have investigated aging brain transcriptomes to identify for age-dependent expression changes and determine genes that may participate in age-related dysfunction. However, aging is a highly complex and heterogeneous process where multiple genes contribute at different levels depending on individuals’ environments and genotypes. Both this biological heterogeneity of aging, as well as technical biases and weaknesses inherent to transcriptome measurements, limit the information gained from a s...
Computer-aided diagnosis of alzheimer’s disease and mild cognitive impairment with MARS/CMARS classification using structural MR images Çevik, Alper; Eyüboğlu, Behçet Murat; Weber, Gerhard Wilhelm; Department of Biomedical Engineering (2017) Early detection of Alzheimer’s disease (AD) and its prodromal stage, amnestic mild cognitive impairment (MCI), has drawn remarkable attention in recent years. Despite the impressive developments in fields of image analysis, pattern classification, and machine learning, no computer-aided diagnosis system has yet been a part of the clinical routine to diagnose the AD. This thesis study aims to propose a thorough procedure which involves detecting the early signs of disease-originated deformations by fully-aut...
Mathematical Modeling and Approximation of Gene Expression Patterns Yılmaz, Fatih; Öktem, Hüseyin Avni (2004-09-03) This study concerns modeling, approximation and inference of gene regulatory dynamics on the basis of gene expression patterns. The dynamical behavior of gene expressions is represented by a system of ordinary differential equations. We introduce a gene-interaction matrix with some nonlinear entries, in particular, quadratic polynomials of the expression levels to keep the system solvable. The model parameters are determined by using optimization. Then, we provide the time-discrete approximation of our time...
META-ANALYSIS OF GENE EXPRESSION HETEROGENEITY IN BRAIN DEVELOPMENT AND AGING Işıldak, Ulaş; Somel, Mehmet; Department of Biology (2022-9-1) Aging is a complex process associated with the accumulation of stochastic genetic and epigenetic alterations, leading to functional decline and increased risk for disease and death. Although some previous studies demonstrated a tendency towards increased inter-individual heterogeneity during aging, whether it is a function of time that starts at the beginning of life is unknown. Its functional consequences and regulations have also not been systematically studied. In this study, I addressed these questions ...
Systems-level analysis reveals multiple modulators of epithelial-mesenchymal transition and identifies DNAJB4 and CD81 as novel metastasis inducers in breast cancer Kagiali, Zeynep Cansu Üretmen; Sanal, Erdem; Karayel, Özge; Polat, Ayşe Nur; Saatçi, Özge; Ersan, Pelin Gülizar; Trappe, Kathrin; Renard, Bernhard Y.; Önder, Tamer T.; Tunçbağ, Nurcan; Şahin, Özgür; Özlü, Nurhan (American Society for Biochemistry & Molecular Biology (ASBMB), 2019-09) Epithelial-mesenchymal transition (EMT) is driven by complex signaling events that induce dramatic biochemical and morphological changes whereby epithelial cells are converted into cancer cells. However, the underlying molecular mechanisms remain elusive. Here, we used mass spectrometry based quantitative proteomics approach to systematically analyze the post-translational biochemical changes that drive differentiation of human mammary epithelial (HMLE) cells into mesenchymal. We identified 314 proteins out...

Citation Formats

H. İzgi, “Meta analysis of alzheimer’s disease at the gene expression level,” M.S. - Master of Science, Middle East Technical University, 2017.