+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Meta-Analysis of Transcriptome Data Related to Hippocampus Biopsies and iPSC-Derived Neuronal Cells from Alzheimer's Disease Patients Reveals an Association with FOXA1 and FOXA2 Gene Regulatory Networks

Meta-Analysis of Transcriptome Data Related to Hippocampus Biopsies and iPSC-Derived Neuronal Cells from Alzheimer's Disease Patients Reveals an Association with FOXA1 and FOXA2 Gene Regulatory Networks

Journal of Alzheimer's Disease 50(4): 1065-1082

Although the incidence of Alzheimer's disease (AD) is continuously increasing in the aging population worldwide, effective therapies are not available. The interplay between causative genetic and environmental factors is partially understood. Meta-analyses have been performed on aspects such as polymorphisms, cytokines, and cognitive training. Here, we propose a meta-analysis approach based on hierarchical clustering analysis of a reliable training set of hippocampus biopsies, which is condensed to a gene expression signature. This gene expression signature was applied to various test sets of brain biopsies and iPSC-derived neuronal cell models to demonstrate its ability to distinguish AD samples from control. Thus, our identified AD-gene signature may form the basis for determination of biomarkers that are urgently needed to overcome current diagnostic shortfalls. Intriguingly, the well-described AD-related genes APP and APOE are not within the signature because their gene expression profiles show a lower correlation to the disease phenotype than genes from the signature. This is in line with the differing characteristics of the disease as early-/late-onset or with/without genetic predisposition. To investigate the gene signature's systemic role(s), signaling pathways, gene ontologies, and transcription factors were analyzed which revealed over-representation of response to stress, regulation of cellular metabolic processes, and reactive oxygen species. Additionally, our results clearly point to an important role of FOXA1 and FOXA2 gene regulatory networks in the etiology of AD. This finding is in corroboration with the recently reported major role of the dopaminergic system in the development of AD and its regulation by FOXA1 and FOXA2.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 058296259

Download citation: RISBibTeXText

PMID: 26890743

DOI: 10.3233/jad-150733

Related references

Induced pluripotent stem cell-derived neuronal cells from a sporadic Alzheimer's disease donor as a model for investigating AD-associated gene regulatory networks. Bmc Genomics 16: 84, 2016

F172. Subcellular Proteome Analysis of iPsc-Derived Neural Cells From Schizophrenia Patients Reveals Alterations Related to Neurodevelopment. Biological Psychiatry 85(10): S279-S280, 2019

Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome. Plos One 7(9): E46159, 2013

Gender-related association of brain-derived neurotrophic factor gene 196A/G polymorphism with Alzheimer's disease--a meta-analysis including 6854 cases and 6868 controls. International Journal of Neuroscience 124(10): 724-733, 2015

Transcriptome meta-analysis reveals a central role for sex steroids in the degeneration of hippocampal neurons in Alzheimer's disease. Bmc Systems Biology 7: 51, 2013

Foxa1 and Foxa2 in thymic epithelial cells (TEC) regulate medullary TEC and regulatory T-cell maturation. Journal of Autoimmunity 93: 131-138, 2018

Association of rs6265 and rs2030324 polymorphisms in brain-derived neurotrophic factor gene with Alzheimer's disease: a meta-analysis. Plos One 9(4): E94961, 2015

Genome-wide location analysis reveals distinct transcriptional circuitry by paralogous regulators Foxa1 and Foxa2. Plos Genetics 8(6): E1002770, 2012

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns. Bmc Genomics 12: 113, 2011

PSEN1 Mutant iPSC-Derived Model Reveals Severe Astrocyte Pathology in Alzheimer's Disease. Stem Cell Reports 9(6): 1885-1897, 2017

Meta analysis algorithms for microarray gene expression data using gene regulatory networks. International Journal of Data Mining and Bioinformatics 4(5): 487-504, 2011

Human iPSC-Derived Cerebellar Neurons from a Patient with Ataxia-Telangiectasia Reveal Disrupted Gene Regulatory Networks. Frontiers in Cellular Neuroscience 11: 321, 2017

Meta-analysis of the association of the cathepsin D Ala224Val gene polymorphism with the risk of Alzheimer's disease: a HuGE gene-disease association review. American Journal of Epidemiology 159(6): 527-536, 2004

Integrated whole transcriptome and DNA methylation analysis identifies gene networks specific to late-onset Alzheimer's disease. Journal of Alzheimer's Disease 44(3): 977-987, 2015

Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression. Neurobiology of Aging 33(9): 2230.E13-2230.E21, 2012