Literature DB >> 22560482

Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression.

Shiri Stempler1, Yedael Y Waldman, Lior Wolf, Eytan Ruppin.   

Abstract

Numerous metabolic alterations are associated with the impairment of brain cells in Alzheimer's disease (AD). Here we use gene expression microarrays of both whole hippocampus tissue and hippocampal neurons of AD patients to investigate the ability of metabolic gene expression to predict AD progression and its cognitive decline. We find that the prediction accuracy of different AD stages is markedly higher when using neuronal expression data (0.9) than when using whole tissue expression (0.76). Furthermore, the metabolic genes' expression is shown to be as effective in predicting AD severity as the entire gene list. Remarkably, a regression model from hippocampal metabolic gene expression leads to a marked correlation of 0.57 with the Mini-Mental State Examination cognitive score. Notably, the expression of top predictive neuronal genes in AD is significantly higher than that of other metabolic genes in the brains of healthy subjects. All together, the analyses point to a subset of metabolic genes that is strongly associated with normal brain functioning and whose disruption plays a major role in AD.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22560482     DOI: 10.1016/j.neurobiolaging.2012.04.003

Source DB:  PubMed          Journal:  Neurobiol Aging        ISSN: 0197-4580            Impact factor:   4.673


  6 in total

1.  Glia maturation factor expression in hippocampus of human Alzheimer's disease.

Authors:  Deirdre Stolmeier; Ramasamy Thangavel; Poojya Anantharam; Mohammad M Khan; Duraisamy Kempuraj; Asgar Zaheer
Journal:  Neurochem Res       Date:  2013-05-03       Impact factor: 3.996

2.  Analyzing gene expression from whole tissue vs. different cell types reveals the central role of neurons in predicting severity of Alzheimer's disease.

Authors:  Shiri Stempler; Eytan Ruppin
Journal:  PLoS One       Date:  2012-09-28       Impact factor: 3.240

3.  Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease.

Authors:  Shiri Stempler; Keren Yizhak; Eytan Ruppin
Journal:  PLoS One       Date:  2014-08-15       Impact factor: 3.240

4.  The Bioinformatic Analysis of the Dysregulated Genes and MicroRNAs in Entorhinal Cortex, Hippocampus, and Blood for Alzheimer's Disease.

Authors:  Xiaocong Pang; Ying Zhao; Jinhua Wang; Qimeng Zhou; Lvjie Xu; Ai-Lin Liu; Guan-Hua Du
Journal:  Biomed Res Int       Date:  2017-11-21       Impact factor: 3.411

5.  Amyloid β perturbs elevated heme flux induced with neuronal development.

Authors:  Chantal Vidal; Kelly Daescu; Keely E Fitzgerald; Anna Starokadomska; Ilya Bezprozvanny; Li Zhang
Journal:  Alzheimers Dement (N Y)       Date:  2019-01-22

6.  Protective effects of Huang-Lian-Jie-Du-Tang against Aβ25-35-induced memory deficits and oxidative stress in rats.

Authors:  Wenbin Wu; Xiaojing He; Shuling Xie; Bin Li; Jinxin Chen; Yanqin Qu; Baiyang Li; Ming Lei; Xuehui Liu
Journal:  J Int Med Res       Date:  2020-03       Impact factor: 1.573

  6 in total

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